Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Created by Berkay Alan**\n",
    "\n",
    "**Linear Regression**\n",
    "\n",
    "**19 July 2021**\n",
    "\n",
    "**For more Tutorial:** https://www.kaggle.com/berkayalan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content\n",
    "\n",
    "- **Linear Methods for Regression**\n",
    "    - What is Linear Regression?\n",
    "    - Simple Linear Regression (Theory - Model- Tuning)\n",
    "    - Multiple Linear Regression (Theory - Model- Tuning)\n",
    "    - Least-Squares Regression(Ordinary Least Squares) (Theory - Model- Tuning)\n",
    "    - Principal Component Analysis (PCA) \n",
    "    - Principal component regression(PCR) (Theory - Model- Tuning)\n",
    "    - Shrinkage(Regularization) Methods\n",
    "        - Partial Least Squares (Theory - Model- Tuning)\n",
    "        - Ridge Regression(L2 Regularization) (Theory - Model- Tuning)\n",
    "        - Lasso Regression(L1 Regularization) (Theory - Model- Tuning)\n",
    "        - Elastic Net Regression (Theory - Model- Tuning)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **The Elements of  Statistical Learning** - Trevor Hastie,  Robert Tibshirani, Jerome Friedman -  Data Mining, Inference, and Prediction (Springer Series in Statistics) \n",
    "\n",
    "- [**Linear Regression by Yale University**](http://www.stat.yale.edu/Courses/1997-98/101/linreg.htm)\n",
    "\n",
    "- [**An introduction to simple linear regression**](https://www.scribbr.com/statistics/simple-linear-regression/)\n",
    "\n",
    "- [**An introduction to Multiple linear regression**](https://www.scribbr.com/statistics/multiple-linear-regression/)\n",
    "\n",
    "- [**How to Develop Elastic Net Regression Models in Python**](https://machinelearningmastery.com/elastic-net-regression-in-python/)\n",
    "\n",
    "- [**Linear Regression for Machine Learning**](https://machinelearningmastery.com/linear-regression-for-machine-learning/)\n",
    "\n",
    "- [**Applied Data Mining and Statistical Learning by the PennState University**](https://online.stat.psu.edu/stat508/lesson/7/7.1)\n",
    "\n",
    "- [**How to Calculate Principal Component Analysis (PCA) from Scratch in Python**](https://machinelearningmastery.com/calculate-principal-component-analysis-scratch-python/)\n",
    "\n",
    "- [**Validating your Machine Learning Model**](https://towardsdatascience.com/validating-your-machine-learning-model-25b4c8643fb7)\n",
    "\n",
    "- [**Understanding Confusion Matrix**](https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62)\n",
    "\n",
    "- [**Shrinkage methods by Stanford**](https://web.stanford.edu/class/stats202/notes/Model-selection/Shrinkage.html)\n",
    "\n",
    "- [**Partial Least Squares regression by Xlstat**](https://www.xlstat.com/en/solutions/features/partial-least-squares-regression)\n",
    "\n",
    "- [**Ridge Regression by Statquest**](https://www.youtube.com/watch?v=Q81RR3yKn30&ab_channel=StatQuestwithJoshStarmer)\n",
    "\n",
    "- [**Lasso Regression by Statquest**](https://www.youtube.com/watch?v=NGf0voTMlcs&ab_channel=StatQuestwithJoshStarmer)\n",
    "\n",
    "- [**Elastic Net Regression by Statquest**](https://www.youtube.com/watch?v=1dKRdX9bfIo&ab_channel=StatQuestwithJoshStarmer)\n",
    "\n",
    "- [**Regularization: Ridge, Lasso and Elastic Net by Datacamp**](https://www.datacamp.com/community/tutorials/tutorial-ridge-lasso-elastic-net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error,r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split,cross_val_score,cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_decomposition import PLSRegression,PLSSVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn import model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge,Lasso,ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeCV,LassoCV,ElasticNetCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Methods for Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A linear regression model assumes that the regression function E(Y |X) is linear in the inputs X1,...,Xp. Linear models were largely developed in the precomputer age of statistics, but even in today’s computer era there are still good reasons to study and use them. They are simple and often provide an adequate and interpretable description of how the inputs affect the output. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### What is Linear Regression?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model."
   ]
  },
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This image is cited by: https://www.researchgate.net/profile/Hieu-Tran-17/publication/340271573/figure/fig3/AS:874657431437319@1585545990533/Linear-Regression-model-sample-illustration.ppm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Simple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Simple linear regression is used to estimate the relationship between two quantitative variables. We can use simple linear regression when WE want to know:\n",
    "\n",
    "1. How strong the relationship is between two variables (e.g. the relationship between rainfall and soil erosion).\n",
    "\n",
    "2. The value of the dependent variable at a certain value of the independent variable (e.g. the amount of soil erosion at a certain level of rainfall).\n",
    "\n",
    "**Example** \n",
    "\n",
    "I am a social researcher interested in the relationship between income and happiness. I survey 500 people whose incomes range from *$15k* to *$125k* and ask them to rank their happiness on a scale from 1 to 10.\n",
    "\n",
    "My independent variable (income) and dependent variable (happiness) are both quantitative, so I can do a regression analysis to see if there is a linear relationship between them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For a real world example, we will use *advertising* dataset.\n",
    "\n",
    "It can be downloaded from here: https://www.kaggle.com/ashydv/advertising-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We will understand the dataset first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ads = pd.read_csv(\"Advertising.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     TV  radio  newspaper  sales\n",
       "0           1  230.1   37.8       69.2   22.1\n",
       "1           2   44.5   39.3       45.1   10.4\n",
       "2           3   17.2   45.9       69.3    9.3\n",
       "3           4  151.5   41.3       58.5   18.5\n",
       "4           5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ads.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ads = ads[['TV', 'radio', 'newspaper', 'sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
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       "      <td>18.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper  sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3    9.3\n",
       "3  151.5   41.3       58.5   18.5\n",
       "4  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ads.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>200.0</td>\n",
       "      <td>147.0425</td>\n",
       "      <td>85.854236</td>\n",
       "      <td>0.7</td>\n",
       "      <td>74.375</td>\n",
       "      <td>149.75</td>\n",
       "      <td>218.825</td>\n",
       "      <td>296.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radio</th>\n",
       "      <td>200.0</td>\n",
       "      <td>23.2640</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.975</td>\n",
       "      <td>22.90</td>\n",
       "      <td>36.525</td>\n",
       "      <td>49.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newspaper</th>\n",
       "      <td>200.0</td>\n",
       "      <td>30.5540</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.750</td>\n",
       "      <td>25.75</td>\n",
       "      <td>45.100</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>200.0</td>\n",
       "      <td>14.0225</td>\n",
       "      <td>5.217457</td>\n",
       "      <td>1.6</td>\n",
       "      <td>10.375</td>\n",
       "      <td>12.90</td>\n",
       "      <td>17.400</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count      mean        std  min     25%     50%      75%    max\n",
       "TV         200.0  147.0425  85.854236  0.7  74.375  149.75  218.825  296.4\n",
       "radio      200.0   23.2640  14.846809  0.0   9.975   22.90   36.525   49.6\n",
       "newspaper  200.0   30.5540  21.778621  0.3  12.750   25.75   45.100  114.0\n",
       "sales      200.0   14.0225   5.217457  1.6  10.375   12.90   17.400   27.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ads.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           float64\n",
       "radio        float64\n",
       "newspaper    float64\n",
       "sales        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ads.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ads.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           0\n",
       "radio        0\n",
       "newspaper    0\n",
       "sales        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ads.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's see correlations between variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054809</td>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.782224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radio</th>\n",
       "      <td>0.054809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>0.576223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newspaper</th>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.228299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>0.782224</td>\n",
       "      <td>0.576223</td>\n",
       "      <td>0.228299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TV     radio  newspaper     sales\n",
       "TV         1.000000  0.054809   0.056648  0.782224\n",
       "radio      0.054809  1.000000   0.354104  0.576223\n",
       "newspaper  0.056648  0.354104   1.000000  0.228299\n",
       "sales      0.782224  0.576223   0.228299  1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ads.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's see correlations in pairplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g= sns.pairplot(ads,kind=\"reg\",diag_kws= {'color': 'red'})\n",
    "\n",
    "g.fig.suptitle(\"Correlation of Advertising Dataset\", y=1.08)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=\"TV\", y=\"sales\",data=ads,kind=\"reg\",color=\"green\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will create a simple linear regression model by using **statsmodel** library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>TV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>230.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>151.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>180.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const     TV\n",
       "0    1.0  230.1\n",
       "1    1.0   44.5\n",
       "2    1.0   17.2\n",
       "3    1.0  151.5\n",
       "4    1.0  180.8"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = ads.TV\n",
    "X = sm.add_constant(X) # It will add a constant to X.\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y = ads.sales # Dependent Variable -Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "slr = sm.OLS(y,X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model = slr.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>sales</td>      <th>  R-squared:         </th> <td>   0.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   312.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 19 Jul 2021</td> <th>  Prob (F-statistic):</th> <td>1.47e-42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:16:39</td>     <th>  Log-Likelihood:    </th> <td> -519.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   1042.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   198</td>      <th>  BIC:               </th> <td>   1049.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    7.0326</td> <td>    0.458</td> <td>   15.360</td> <td> 0.000</td> <td>    6.130</td> <td>    7.935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>    <td>    0.0475</td> <td>    0.003</td> <td>   17.668</td> <td> 0.000</td> <td>    0.042</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.531</td> <th>  Durbin-Watson:     </th> <td>   1.935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.767</td> <th>  Jarque-Bera (JB):  </th> <td>   0.669</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.089</td> <th>  Prob(JB):          </th> <td>   0.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.779</td> <th>  Cond. No.          </th> <td>    338.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  sales   R-squared:                       0.612\n",
       "Model:                            OLS   Adj. R-squared:                  0.610\n",
       "Method:                 Least Squares   F-statistic:                     312.1\n",
       "Date:                Mon, 19 Jul 2021   Prob (F-statistic):           1.47e-42\n",
       "Time:                        07:16:39   Log-Likelihood:                -519.05\n",
       "No. Observations:                 200   AIC:                             1042.\n",
       "Df Residuals:                     198   BIC:                             1049.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          7.0326      0.458     15.360      0.000       6.130       7.935\n",
       "TV             0.0475      0.003     17.668      0.000       0.042       0.053\n",
       "==============================================================================\n",
       "Omnibus:                        0.531   Durbin-Watson:                   1.935\n",
       "Prob(Omnibus):                  0.767   Jarque-Bera (JB):                0.669\n",
       "Skew:                          -0.089   Prob(JB):                        0.716\n",
       "Kurtosis:                       2.779   Cond. No.                         338.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    7.032594\n",
       "TV       0.047537\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params # Main parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    7.0326</td> <td>    0.458</td> <td>   15.360</td> <td> 0.000</td> <td>    6.130</td> <td>    7.935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>    <td>    0.0475</td> <td>    0.003</td> <td>   17.668</td> <td> 0.000</td> <td>    0.042</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>6.129719</td>\n",
       "      <td>7.935468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>0.042231</td>\n",
       "      <td>0.052843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1\n",
       "const  6.129719  7.935468\n",
       "TV     0.042231  0.052843"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.467389700194647e-42"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Signifigant Level - P value\n",
    "\n",
    "model.f_pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f_pvalue:  0.0000\n"
     ]
    }
   ],
   "source": [
    "print(\"f_pvalue: \", \"%.4f\" % model.f_pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "312.144994372713"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    15.360275\n",
       "TV       17.667626\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.tvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3314.618166868649"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sum of squares error of model\n",
    "\n",
    "model.mse_model #This is very bad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.611875050850071"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6099148238341623"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.rsquared_adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17.970775\n",
       "1     9.147974\n",
       "2     7.850224\n",
       "3    14.234395\n",
       "4    15.627218\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicted Values\n",
    "model.fittedvalues[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.1\n",
       "1    10.4\n",
       "2     9.3\n",
       "3    18.5\n",
       "4    12.9\n",
       "Name: sales, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#real values\n",
    "y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sales:  7.032593549127695 + (TV* 0.04753664043301975 )\n"
     ]
    }
   ],
   "source": [
    "#Manual formula of our model\n",
    "print(\"Sales: \" , model.params[0] , \"+ (TV*\",model.params[1],\")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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azoQzFhLLhUMFv6KMceLNOBmrDd1tQrns7ssYGhri0T98lN5DvUyYMQFvmZeB7oGY7OmJ+BXidViP1sRy4VBTj6KMcRJJtTCcDT3UhOL3+3n0Dx+l+aVmSi8q5XNbPxdTmGVo3L97u+H2i9WEMxbj/lXjVxQl5Rknw5lQeg/1MufaOax8ciUej2fYMEtHY3eifxofaaTkrBLm3zqfge6BqDnwYzXhpDu/fqaINypJBb+ijDISDU1MdcbJUBPKhBkTAkLfvT7ceUJDTAu8BZScVUJ3azcD3QPUrK4Bosfmx2LCGQ1x/9FMWpFQwa8oo4hsCk0MZ0KpW1cXJHyHE9pAxGRrsTh2Y3FYZ3t+/WgMNwcjEmrjV5RRQjaFJqYidcJwIaaxCP1Yz5+t+fWHw+2P8W30seEjG4LMVpFQjV9RRgnZFJqYChNKMtW8stWEk44ZwpFMWi3HTkbeJxc815qWWVFix52S2BjD3dvvjphOORNtSUTQRXO6RnuRhR7f7/cHzRMYyVQM6TLDOcdpfKSRYxPy2VM6ng8unMp7p4bY/2+3hk3LrBq/oowi3Fpy76Fe/H4/m9duZtmaZQAZt/cnakJJRGMPJ1hDcwGNpKYfTz6keI7782+/zhOvvE/zlRW0js+zVpwaAqCY4unh9lPBryijBLeWvHDlQgSh7sE66h+sB0AQfLXJCZpMEo/TNV2CNVWk0gxnjGFPWxebGlvZ5Gtj77FuuGhaYH1pSSHnNHcy881DbMCTF+4YKvgVZZQQqiU71D1Yx7bvbGPCjAk5l4og1hFDNvk3IpHMDGFjDL4DnTzja+VZXxvvH+4JWl8xqYhLBgzTX22h8sQAAlStqub+7V3t4Y6ngl9RRhGhWvLSNUvx1Y6NVATxCtZUOFrjOUa8zmq/3/Bmy3Ge9bXxTGPrGc7aWVMnsLyqnJuryrnk7MkAQeUml963FL4cvt0q+BVllBE6K9VNrFExmSKVUS7x9DcVjtZ4jhHrDOEhv6Fh/zE22Zp964m+oOOcM6OY5VXlLK+qYOFZE4e915FQwa8oo5BcSEWQyiiXePqbCn9AIsVrIjmr870FbNl3hE2+Np5tauNQV3/QuS4oL+HmqnJWVFcwr9QbNi9SpL6XUFIWrv1pE/wicjbwY6Ac8APrjTEPishU4FFgDtAM3GGMOZaudijKWCRb49gdUu2Mjae/8eTxiTQaScSn4DbDDQ752bzvCJvmeHl+VztHvxdsiq+aOZHlVRXcXFXOuTO8Cffd3+AfCrtPuuL4RaQCqDDG7BCREqAB+EPgs8BRY8y3RORrwBRjzFejHUvj+BUlMbK5pKBbU3WIVvUrln7Ea3N328Tv2nZXYNtYRyPRjhFK3+AQr+89zCZfGy/saqOz71TQ+ovPnswK24wza9qEsMeIRri+ezyezMbxG2NagVb7/y4R2Q3MBG4DrrY3+xHwMhBV8CvKWCBdszqjfR9pYnHGxmMSirW/0fwBQEyjkVh8CicHhnjlnQ42+dp4cXcH3f2nhb0IXDZrCsurLc1+5uTxw16vaMRzrzNi4xeROcClQD1QZr8UMMa0ikhphH3uBu4GmDVrViaaqSgjRjYlV0s3xhh2bNhBX2cfwmnh1N3ezeN3PM7tj92eUnt8uPMP5w8YzowT7Rh9xtBzwxye9bXz0p4OTg6etrZ4BJbMncby6nJuWlhO2cSixC9kEqRd8IuIF/gFcK8xpjPWm2SMWQ+sB8vUk74WKsrIku2Tj1JJw/oG+jr7wED9Q9bEsvJLyskrzKO7vZuWuha2rN3C0jWWgE1HfH6s/oBoo5HQY3T1n6Ltmlk8f6Kb/zhynIGNbwb2y/MIy86dxvKqCm5cWMZ0b+R0yZkirYJfRMZhCf2fGWOesBe3i0iFre1XAB3pbIOixMJI2sJHevJRpvruvOCaaptYuHIhlUsqee+37/H+i+8zftp4lnxpiSVQJ4Z3xqayNOJws4JjMeOc+6lqXtjVzl/8aDuv7T3E4NBp/XRcnnDledNZXlXBDQvKmFJckHBb00E6o3oE+D6w2xhzv2vVU8BngG/Zf59MVxsUJRaywcySDuEWC5nse+gLzhjD0MAQ46eNx1vmZdmXlwW2c5Ou+QiRbOLRzDidfj9HP3Y2m3xtbNl3hFP+08K+IN/DVfNnsKK6nGsvKGPS+HEJty3dpFPjvwL4U6BRRN60l/0tlsB/TET+AvgAuD2NbVCUqGSLmSWScKtZXZO27JKJ9j2ZEYIj/BsfaaSnowcRwVtmhSu6navuY8cTnx9vuyLt4zbjHOrq592lZ/Gr413sOXIC/y9PBLYfPy6Pay6Ywc1VFVx7QSnewtyYGpXOqJ7XgUhX/bp0nVdR4mGkzSwQWbhtfWgrzS83B8oVplobT6TvyY4QjDFsXruZno4e+o72UTS1KJBQLpwwj9Uen0i7ou1TcccCnvW1su7hLWzffwx31Lu3MJ9rLyhleVU5V59fyviCsHnQsprceD0pShoZKTOL+/yhwq1mdQ3NLzfTsbMjUK4wHSORePqe7OjIEaxNtU1U1lRSuaQSgwnY/KtWVoWdXBaLPT7edoXb58l/f52n6j7kgwVTefdbLwVtP7EonxsWlLO8qpwr502naFzuCXs3WohFGfPEM5Eo2jGSdZCG7uP3+6lbV5dUu2I5Zzx9T/ZaubVsB0fLXnTXooT7lUi7jDE88e3X+fW2D9lTOp62icEO2KnFBdy4oIzl1RUsPWcaBfm5V6lWRMJO4FLBr4xpotmQYxVo6XSQxjMzNJFjJ1rlKlqbhnsJpiuKKJZrZYxhb0c3zzRaSdD2tHUFrZ/uLeCmheXcUl3B4rlTyc/LPWHvJpLgV1OPMqaJ1YYciXQ6h9OdXTOWvkcahURqUywvwXTMJh5uJm7TwU4rvbGvlfcOBeeyL+kb4vyOk1zQcZKbb57HFX9YNWrmTURCBb8y5omn0lMo6XIOZyq7ZrS+hwpxv99P7W21dOzsYPGXFp/RpprVNSMSIRXuWm1eu5nnf/M2G7t7eWt8Hh8c7Q3aZ+bk8VzS72faqy3cuHweV6xbFjiGJ477n6uo4FfGDMNlW3QTz48+UefwcO3JVHbNcH0PN5KpW1dHx84OSi8qpWZ1zRlt8ng8SWe9TLT9hSWFLFi5kII/voB//s1uNp3q4+DiUjjZD3b9kjnTJnBzVQUrqsupnjmJHRt20L9iQlZmL003auNXxgTptsPH61iMJ/tjOuzhsRCpX8PNLUhF1stYGfIbtr5/lGd9rTzb1EZ7Z3Au+/NKvVbGy+oKLigvCWvzH6nrmwnUxq+MWTJhh4/HJBNPe9JhD4+VWEcyoYIz2ayXwzE45Kf+vaM842vl+aY2DncPBK2/sGKiXaWqnHllJcP2Mdr30YoKfmXUk85JWomYZLJh0lgsxOtcTkXWy0gMnPLzxr7DbGps5fld7RzvHQxaf1HlpEDhkrnTi5Pqt9OX0TwSUMGvjAnSOUkrEefwSE8aG45ERjKxvgRj7Xff4BCv7bWE/Qu72+kKKVyyaNbkgLA/e2r8hUsikQ25m9KNCn5lTJCJ0Mho3zPdnmRJ1Lkc7iXo9gkYY9iyNnK/ewdO8crbh3jG18ZLu9vpGTidy14EPjJ7Kiuqy7mpqpyKSckVLglHMmbBXBolqOBXRj3pDI1MNDFYNhRCH67tiYa5uteHztTdsnYLdQ/WUVlTye2P3c6W+7fQ8FgTm/sGeHd2CS+/3UHfoD+wf55HqDlnKjdXVXDTwjJKS9JbuCRRM1yujRJU8CujnkjaqzEmSHuNV0NL9MeeqlDNZDTMWNuejPPTrT0bY1i2Zhkt9S0ATLq8gl/saGHT9HG8es1ZDHb1gM+aWJXvEa44bzorqsu5YUE5UzOcyz5eM1y2ZHiNBxX8ypggVHvdsWEHgrDorkUAcWtoyf7Yk5k0BslpmJkSVI6Zp6WuhfoH69n6813snVFEy2cX8p/HOhl6fGdg24I8Dx+bbxUuuf7CMiZNGLlc9vGa4XLFWe9mWMEvIsXASWOMX0TmAxcAm4wxg8PsqihZhVuz7+/qx1frA0lM8I3kjz1ZwZ2ptjesb6D9eC+7L5rO8wV+Ds70YjwCg5aTtgC4ZmEZK6qtXPYlRSNfuCRRM1y2O+tDiUXjfxX4qIhMAV4EtgN3An+SzoYpY4tMOsZSJfiS+bEno7Gnov3pFFRtJ/rY5Gvl0Q8Ps2dg0PLKnm3F0+cPDDH7gy5uufQsPv/lKykuHHlh7yZRM1y2O+tDiUXwizGmV6yKWd8xxnxbRH6f7oYpY4eRcIylQvAl+mNPhakl2fYP1/ZwL2LnvO5lzveWY70862tjk6+Nhv3H3A1lXP8Q5xzoZvauo8xq7UH6TrGkZg4TCrLT0hyvGS5bnPXxEJPgF5GlWBr+X8Sxn6IMy0g5xpLV0JL5sadCY0+m/cO1vcBbwED3QNBL4PE7Hgfg9sduDyz71b+/TgN+Gifk8VbLiaBzTJ4wjhsXlHHeh90cX1dPvoG+o31Mv3A63e3dHKg/MGwfR5J4nNqpctZnklgE+L3A/wF+aYxpEpFzgN+ltVXKmGEkbOWp0NCS/bEno7En2/5obXeEftCLeO0WWuqsaJzH/+012j5SwS9e2sf+ITvG/qj1Z1pxATfZqRJqzplGvkfYvHYz9bbQd8osAjTVNmW1KSReknXWZ5qYk7SJSLExpmf4LVOPJmkb/aSz4Eg4UmVeStQ3kUhit1S3P1Lb3W0zwCFvPkeumc3WgQEODPmDjlFaUsjyqnJurrIKl+R5TjvQnWNMnjv5jDKLglA4MTtj3EcTkmiSNtvM833AC8wSkYuBzxtj/ir1zVTGIiPhGEuVhpZInHsqRhypaH+0tnvvXMDvtn7A26XjOVqcDyf7Ausm9p3i/I4+/uZfruey2VPxeMJHubhHFaHLkymzOJrJVJBDLKaeB4CbgKfshrwlIh9LeUuUMUmQEFxZxdI1LiFoYOma9An/ZCYnJXveVNiEU9l+v9/wZstxNjW2ssnXRsuxkzD3dGbLGR4PSwrHMf2VD6noHESAwSfeRlxCPZTQlxNkp6MzW8hkkENMTlpjzIchN2so0raKEg+OEJw8dzIGy+y49L6lYKClvoUdG3ZkxPSSabLBJjzkNzTsP8Yzja0819RG64m+oPUVeR4+/tG5VL7ZQet/bkeAJfcsYdmaZWFHKLFc+0z1L1eeA4fhghz8fn/UGgjxEovg/1BElgFGRAqALwG7Ez6jooSw6K5F9HX20VTbFBCABsPx94/T39Uf80Oea/lSRkIonhrys/X9o2zytfFsUxuHuoILl5xfVsLF/X4ukzxu/8oVeDwezE3n83hDGwDL1iwLO0LJpmu//eHtQVFJTp3gbH0OIHqQQ4G3gLp1dSm9trEI/r8EHgRmAi3A88AXEjqbooRBRAICJZnwxlzLl5IpBof8bN53JJDL/mhPcOGSBRUTWVFtOWjPK/UCZ5aBvP2x2wP/O3/dgihbrv32h7fzztPv0HWwC7DqALvrBGfzcxAu0qtmdQ116+pSfm2HFfzGmMPoLF0lzSQ7ISnVYaG5ZioIpf/UEK/vPcwmXysv7OrgxMngDCsXV05iwbEBrpxazC1fWHLG/rGMRkJfApCekNxY74UxhoHuAbpbuyk5q4TGRxqpe6COvqN9zLl2TqBOcLYSLsihbl0dNatrgNRe24iCX0S+A0SM9TTGfCmhMyqjilQJyFRE9iT78nDIJrNFPJwcGOKVdw6xydfKS7s76Oo/XbhEgJnH+7n4pJ9bF5/NpL4hfE++y5QUaeSpuvahxHMvQl9AR/YcAaBoahErn1wZZCPPNoaL9KpZXZPSaxvtSmwHGqJ8lDFOw/oGtty/JTCd33l4G9bH93iEPvR3bbuLqlVV+Db6go4/3DHcLw9nn1j3dx/HMVs4+zptc/wN2URP/yl+/dZBvvCzHSz6pxf4y5828OSbB+nqP4UAs4/281nveDZ/7Rr+6kAfM3/+Dru/9UaQgAklkT5GenEnc70SuRciVkbQ7vbuwLLi0mLq1tVl3b1zEynSy23jd5PstY2o8RtjfpTwUZVRTyrtusmGNzasb6C/s/+MCUIt9S1x50tJt9kiFXT2DfLS7g6eaWzllXcO0X8quHDJsnOnsbyqghsWlLJ3/Q58G338+lfvYoyhaGoR3jLLjr/0vqXs2LAjJRPB0pGrJpF74ff7qb2tNjBT2FvmxVvhpfGRxqTakgkiVS9zbPypvLaxTOCaAXwVWAAEyt8YY64dZr8fALcCHcaYKnvZN4G7gEP2Zn9rjHkm7lYrI06qBWSi4Y3uFMuT504OCH1frTUvYOaSmXHnS4lmthgpW//x3gGe39XOs42tvP7uEQZcM2jH5QkfnTeDm6vKueHCMqa4CpdMt/thjKG7vZuS8tOx+Zv/YzOIlT4BEn95p2peQqRjx2pCMsZQt66Ojp0dzLl2DiufXBkQmiVnlVDgLchaoe8QzrcSOhEuFdc2lqienwGPArdgRfh8htOCOxo/BL4L/Dhk+TpjzH/E0UYlS0m1XTeR8MbQF9Dx948DBP1QEnHsbrl/C72HevH7/XjLvGy5f0tA+8qUrf9wdz/PN7WzydfKln1HOOU/PbQvyPdw1fwZnPdhF0unevnYZz8SsR/GGI69d4zBnkHKLyln1VOr2HL/FuofrKdySSULVy5M+uWdjnkJbnOd811EIvp+HCG5+EuLA3V+3TmILv/8GZkLsorQF607LNXBGY0le21jEfzTjDHfF5F7jDGvAK+IyCvD7WSMeVVE5iTcMiXlpDpSZSRSLYQjlS8gt9mi9KJSug524a3w4tvoo/nlZroOdlH9yeq0af4dnX0829TGpsY26t8/gkvWUwjMbT/J9edN5/NrrqDxP7fh++Ve8sNo56HmlwN1B3jvt+/R/mZ78D0T61o5Wj8kZ5qJ9j0eEjXfZcPEuEQIdWL7/X7eefodOnZ2AGeOxpIlFsHvxIG1isgtwEGgMolzflFEPo3lPF5jjDk23A5K8oSLjti8djNFE4sC2ms8wixddt1ESOULyD20djT8xkca6enoofdQb0CbTOUL9ODxk3Yu+1a27z+G22fnLczn2gtKWV5VzlXzZ/Dmd7fi2+hj4y/fASJr56Emgob1DZzqO0XbW21s+842JsyYQPkl5cy9bm5Yx+FICstkzXepfAFlgnD+srp1dXQd7KL0otK0+JpiEfz/LCKTgDXAd4CJwOoEz/dfwD9hhYn+E7AW+PNwG4rI3cDdALNmzUrwdAqEf7Aev+NxWupaWHLPkqAhdaxmjHTadeMhHS8gt9bojCSKS4sRkZTNovzwaC+bfK0809jGmx8eD1o3sSif6xeUsaKqgivnTacw3xPow9L7ltL4SGPQ90j9c/oBMNA9QFerNalpwowJdLd3Y4xh72/2BkYxI/nydpMO8102E8lfVv3JampW1/C9xd8LbJuqexLLBK6n7X9PANckczJjTLvzv4hsAJ6Osu16YD1YaZmTOe9YJ/TBcjRYAMF6iBJx6mXDsDpdLyC3UHe+A0GzQuN1hr53qJtNtmbvO9AZtG7KhHHcsKCMuXuPc+nUYmruuAQIHqkBbF67mZ6OHjweDxNmTBhWO3eW16yuofnlZo7sOULfUSsnT/kl5cy7ZR6DPYMj+vKO1O50zAvIVsL11xlxuknVaCzaBK67gJeNMXvFOssPgD8C9gOfMcbEXX5RRCqMMa32148DvmjbK6nD/WCJCMWlxVSvqsZX67OKjpPYMHIkh9WOoHVrtk4bUuFYjDSScGaFxjL83tvexTONlrDf09YVtG6SCOd+0MXNVeX82VeuZNuD9fh+8TZ++yUCnB6pGTAY6h+sB2DxXy/GYGLSzp1ol+7WboqmFlFcWkxPRw/drd0M9gwGma6yxSaeLf6jTBHaX2MMtbfV0t3anRZTajSN/x6syByAVcBFwDnApcBDwEejHVhENgJXA9NFpAX4BnC1iFyCZeppBj6fcMuVuAh9sEQkkA3TIZ0/qlQ7lkN9FhBsqkq2H9FGEgXeAnZs2BHY1t0GYwy7WjvZZAv7fYeCaxeVTyzi5qpyVlRXsGjWZLY+YIUb/s8v3gbOfIm4R2q9h3oBK0Pm0jWnTR7DhSk623grvIHrXlxajLfCS4G34IwZrSMtWLPJf5QJIvV360NbKb2oNPBiTuVoLJrgP2WMcRy7twI/NsYcAX4rIt+OoTOrwiz+fgJtVJIk3IO1ee3mgPboTOhJl0aV6hQImUoKFm1CjZvNazcz4Y4FgWicD472Bq2fOXk8K6rLWV5dwSWVk4MKlwxnznCP1CbMmBAQ/qHtaVjfEPFaOjlsQm35jY80MtA9kHV5iLLFf5QphlMynBdzKkdj0QS/X0QqgGPAdcC/uNaNT/rMSsYIfbDgtG2/sqaS2x+7PW0aVTqEdCRnWDpm14aOUpwJQQtWLqTwjy/gBz/+Pd/98DCd/29z0H5zpk1geXUFK6oqqJo5MWybYjFnhG7j9/sDL+xla5YFzeqMdC2d++8I/VwQpNngP8oksfY3Vf2PWHNXRG4FHgbygF8bY+6yl18F/I0x5paUtCAGtOZuanA/WE6ctFPhKp2JyNwjDodUCGljMlund8hv+Nl36nj9WA9vFUB7Z3Au+/NKvayw689eWFEStS3RzBnuF/QZ26zdQt2D1ojDiTSK9Vqm2tymZD8Sb81dY8zTIjIbKAmJtd8O3JmGNiopINqP2708kxpVOiI0MuX8GxzyU//eUZ7xtfJ8UxuHu+1c9naxqgvKS1hu2+znlZVEPlAIsZozztjGtu1v/c7WmEI6Q88Z7bsydogazmmMOYVl6nEv64mwuTLCxGtLz5QgSLWQTqfzzxjD4JDhjX2HrcIlTe0cD8llXz1zEsury1leVcHc6cUJnQdie/mGq1trMEyYMSHwfTRGu0RTYHTkkjwx1dxVsp902NJT8QNLh5BOh/Ovb3CIH363jteP9/KW+OnqOxW0/tJZk1leZQn7s6dOiHCU+Inl5Rtq72+qbRrV0S7RFBggJ2slZBvR4vivMMa8ISKFxpj+SNsp2UGqHZ6pisRJV4TGorsWBY7vEG8/Tw4M8bu329nka+el3e30DAy52g3n5+dT6TvMbUtnc+v/XjbiQnUsRLtEU2DcqRtC14UqNzoqiE40526DMeYyEdlhjFmU4XYFoc7d2EmFwzMWx+NIjB4cknkpdfef4qU9HWxqbOWlpnb6Xc9/nkeY3TnI7N1HqT45hHfAn1V5+B1Gu1CLFgwADBsokKsV1NJB3M5dYFBE/geYKSIPha40Wnox60iVLT2W0UO8wiZV/oRETFonTg7y4u52nmls5dW9hxlwFS7x+A1VheP45B9cyORXPmD/c030dPQEFSvJNqEa6VqOxAshHeccLhgg2rpMzfHIdaIJ/luB64Fr0VKLWU+qbenuH5+Tl96ZQTiSGlSsJq2jPQO8sKuNTb423nj3MINDrlz2eR6unDed+S3dlDceYmDfcTo37eeEMZScFRyGmc2O09Dw3L7OPpatWZaxe5QuzTqaAuP8H26diKTc5JnNJPPSjRbOeRioFZHdxpi3km6lklZisf/G86C4f3x+v5++o33U3lYbVNVopDSoSBrh4e4BnmuyUiXUvXeUIVcy+3HAOR0nueHc6dx93zJ8/287vl/tZf7KKhrftQLXejp6guLiM+k4jfdHHJq8ra+zj/oH6zlQfyBoQl667lG6NOtoCowx5nR65ij3KB3hw7G0O5OjrWRfurFE9RwRkV8CV2Dl2HkduMcY05Jc05VUE83hGc+DEvrjq1ldQ+1ttTS/1MwDsx+wEry5ZoFmGvdLqavQw57S8Tzx9ed5+9Qp3C6rCQV5XHtBKSuqK7hq/nTe+u42fBt91Dq57FdWYTj9A/V4PHgrvGnJjRKNeH/E4YSuMxO7pa6F9ZevD3qBpaPt6dKsh1NgQs8Rug4yn+At0z6FVLx0YxH8/wM8Atxuf/+UveyGxJuupJpQDdD98C26a1FcD0q4H9/KJ1fywOwH8Hg8YePNkyVWjckYw1P//jpP1X3IhzfP4p1TdiTOoBV+WVJo5bK/2S5cUjQuL7BvqBboVHdya4+NjzQG5dvPhKYY7484ktCtuaeGxo2x5epPBenSrIeb3xBu3Y4NO4LKY/o2+vBWeJl/63wGugfSNnIbCZ9CKl66sQj+UmPM/7i+/1BE7o2/uUq6GO7hc5ZB7A+K+8fn5KhxUgRAajWoWDSm5sM9bPK18ayvlbeOdsL8SWAL/Unjx3GJEa6YPIHPfLGGwvy8M84RTgs8UH+AhSsXBgl5Y0xGQyMT/RGHE7ru0Quk3z9hjFXFzc3mtZsDfobh9o32oo8WDBDu2O7n38lE2nWwi4HuAWpW14BJz8htpHwKyb50YxH8h0TkU8BG+/sq4Ei8DVXSR6wPX7wPilsI+zb60lKlKdpLa8rtF/LGi3t51tfGrtbgwiXTigu4cWE5y6vKWXruNPI9Mqy/IpzduHLJ6SqiOzbsQJCAySxTTuxEfsShL7Lu9m7qH6xnyT1LWLZmWdr9E8aYoCpuy9YsC2R8dfwMkc6ZatNI6PPfe6iXoaEhau6tOT0CJn21nEbKp5CMOSsWwf/nwHeBdVg2/s1EKJeojBzDPXzDRUpE0yzTOWnIfbzGjT5eemoPe8rG03zT2bQc64QXTgv80pJCbq4q5+aqchbPmUp+nifSYePuQ+AFVOsLFCDPVBhgvD/icC8yRwg7tv5MTuxyzun8jUa6TCPOPW18pBG/30//sf6g6nKOSS/V99GZB5VJn0IqIvgiTuDKJnQC1/DEOull8tzJzFwyM6h4tcEEFV2PdPx0RC0YY2g62Mkzja3U/mYPR4vHBa0/a1IRN1dVsLyqjEWzppDnEvbxtmG4PkS7hukW+vFOlgurNa/dQuHE01pzuiNLnHM6M2nBcpg7GV+j7pei6+w2Rzo+GiBQWtSZj5GO++iE0Ib+lg7UH+D4+8fT+uzEOmqSCBO4VPBnMfE4PIcTHjs27KC/sz/g0HSmv7fUt6T9IQ3X3jc/PM6zvjae8bXy4dGTQesndQ1wUb/hvn+4mktmTQHg8TseBwiYENJlhknFzOd4Cf0R+/1+6tbVUeAt4PLPXx4k3IZ7caWqrfE8e4lcr9D9Prf1c0GVwGLpi3PdHIdu4yONlJxVwvxb59Pf1c8r33iFoqlFeMu8Kb+P7t/c5LmTqVxSecZvy/0SjnSMZO5fLPtHEvyapG0EiOWGxWMHjcWU4ThrG9Y34K3w0lTbFDivU4IvnQJuyG9o2H+MTb5WnvW10XqiL2j91J5BPjqjhM/9+SJ2/u9N7H+pmd3tfVz85Eq23L+FljorenjL2i0sXRPePJCs4Mt0GKCD25Hu3PdxxeMY6B4IegkMdA8E3f9oDtBkiPXZS/R6he7Xe6g3MEfEHTzgRKRFeuGEOnRLziqhu7Wbga4BMFA0tSjwMkn1fQz1Kxx77xgiEjTKjnauVPg5krn/KvgzTCw3PBE7aKz59Z0SfE5agu72bowxaSnBd2rIz9b3rVz2zzW1c6grONff/DIvy6vKmfP2MUr7hli2ZhkAC59cSe1ttXTs7OB7i78HWHVmnSG1r9Zy4LnrkSY7AkiF3TQZ3H4GR3Ptbu2m+eVmug52Bb5nwt8Qy7OX6PWKNEfk/Zfe5+FLH+aiT10EQmB9qPnKfb3cgtfBMbc0PdpEzT01QUpCtHYlgtuv5g6giCWiaaTTSgwr+EXk740x/2z/r5k6kyDWG55oiJjbkescx/0doGZ1Dc0vN3NkzxFOHj2JIJRfUh4QoMn275TfsHnfEZ61hf3RnoGgbRZUTGRFtVWl6sSv3qZ/55FAX+G0prfyyZUBoQ8EXgqOPdnv99N1sCsQc5/sDyfdTuxY2xBwdD/SSE9HD4d3H6ZoalGQNpnuF1C0Z8+9XSLXK9x+826Zx7F9xzjxwQle+eYrFE4ppOLSCg7UHeB48/Fh5zMEBTSsscyambiPiY54Ev19p5Jo2Tn/BngN+C9jzCX2shHJ1DmabPzxOLYSsZ9GGlG0/b6NskvKAlpzd1s3BsOpnlNMOXdKUg9d/6khfvidOt443stbHsOJkMIlF5/t5LIvZ/a04jOuwxl+CUdrq20KHCM0JS8QiNV22hxPHyKZ29JpN48V574bYziy5wjTL5wOZMbf4G7Dg3MexO/3B2zkQNjRaSLXK5xTtqe9B4Oht6OXvHF5FE0tCoSKDjd6cIhkaklHNE+yGWwz4U9KxMb/NtZs3XNE5DVgNzBNRM43xryd0taNIcJqKRGEfrzaRKQRhWM6aKptYvKcyZbAbOui/1g/hVMKE7Lx9w0O8fLbh3jW18qLuzvo6g8uXDI/P49Zu47wBzWz+F9/deYPN6LW4xL6zg/IiQ8Hzhi+93T0BCaWxSr04/WfZBKnLcaYQGRKd3s33jJvxhLGOdE6To4msHwr7vsSOqp0iLVtbpOkexRqMJghg2eCh+LS4piEfiRTUyLtipVkR4gj5U9yiCb4jwF/C1xtfy4EbgK+Zgv/ZWlv3SgklhueqP00kjCt/mS1Fflwfx11D9Zx8shJxk8bz9X/eDUGg2+jLyYbf0//KX73dgebfG38bk8Hva7CJR6xCpec3XiY8w+dpKR/+Fz28QzVD9QfCKyH0yar3kO9QQ7BSC9R97Xt7+yPqZhHpnFrv06W0PJLyuk62IW3wpsRf0Pg2av1UXNPDQZD/YP1vPyNlwMaeCrPb4w1K7zrYBdFU4sA6GnvYWhgiJ6OnoAzP9wLZqRNc4nWrR5pfxJEF/w3A98AzgXuB94Ceowxf5bWFuUYjqnM0RwdIpkNYrnhyTzU4YRpzeoaPB4PS9csZet3tgZC3Bwh6pwv3HE7+wZ5aXcHm3ytvPz2IfpduezzPMKyc6dxc1U5Ny4oZ7q3IGjoGm90B5w5qczRzj/x6CcCx9q8djMH6g/QdbCLxV9aHPWHE6rdgzWLc/LcyVmXtte5D9WfrA5E8Tihiu6onnTb+N3PHpx2nno8npjSMcR7Picip6ejh76jfRSXFVN+STl5hXlBE+pCz5uo4E0liYx4suGlFS0t89/ajXwL+ClwKTBDRF4Hjhlj/iDtrctyGtY3sO+FfVQuqaSgpICBrgH8xs/BrQc55/pzwobfxXrDk9UmHJxQuTt/dSf1D9Qzfvp4ejp66D3UGxCyocc90TvIC7vb2dTYymt7DzMwdFrYj8sTrjxvOsurKrhhQRlTigvCnhcIJM0KF58dy0swkumqqbaJyXMnU72qOup1jLb/wpULOf7+8UC7RlroO4TmSHLf90yNRpw2gHUPRSRgTku1OcKJKOtu7aaypvKMyYWVNZXDOoujfc9WRvqlFUs453PGmG3ANhH538aYK0Vkeroblu0YY+jr7KOlroUPt3xIxaUVtL3ZxkDXAOO84zjVdyps+F08NzzehzpaqNx/V/83+ePzAyYEb4U3MMtx6X1LOdLdz/O72tnka2Pzu4c55cplX5Dv4ar5M1heVc51F5Qycfy4IOEKhBXiWx/aSvPLzax8ciUej+cMW3osL8HhIkyiXcdIpi/HUewmk/bV4XD3KdzfTJEJc0S4EYa1gqhx/KOBkXxpxTVzV0QuNiNQlCVbo3qMMQHH48kjJ/HbZpDismK8Zd6MRRi4cc9m9Hg8+P1+am+rpWVzC/kT8gPtqlldw6b/2MybYvBNGkf9+0dwyXrGj8vjmgtmcEH7SRYOCdd+eRk7NuwITFEvnGj9KB1BDpwx+9SJxQ81xyxcuTCoUpT7+oS7NslGP7j3N8ZQvao6bDGPVJt7siFCKBme+NQT9J3oC7y4nXtaNKmIP/rpH6X0XLl+rbIViRDVE1uWK5uREPrZjIiwbM2ywDDYk+8hb1xeUL1WJ0+4I+AcrbdhfXqqWV5292UUeAuoW1eHMQaPx8Odv7qT/An5eDweOgvz2LOknDvX1/HFYyfYcKSTLe9ZQt9bmM//uvgs/vtTi9jxDzfwn59cxOXGw75Hm9iydkugylPdg3X0d/azee1mfBt99Hf1s+iuRUFC0+PxsPLJlSz+0mJ8G31s+MiGwPT2cNq2cz3CCf1wJqRYFZbQ/UWsNBVVK4NHGlWrqgIvMPexjTExn8tNw/qGjN73VGOMofzS8sBcCbcTtvzS8oSuSTRy1WSTq+jM3SRwNP6ejh6MMQGNPxB+5wqBg8xEkDg200BxjtU1/Nftj1E/u4TmC6bQPn08PL07sP3EIqtwyYqqCq6cNz2ocInTZgieHQkECn5E05I9Hk+Qo9kYQ+WSypgzYCYb/RBt/5lLZga2c4T/jg07ePyOx5m5ZGZgwtiWtVtoqW/h3BvOjXlGcDbMzEyWSGaykay8pqQOFfwJ4jbzGGOYe93cgI3fGGOF39kOqgV3LsC30UfjI5awdBf/SDXOD7b11BAPb27mnj2tHJo/EeZPDGwzfmCIJRPH89mVF7PsvOkU5Ece+IVGCXnLvEGCa7h5BaHatsFQtbIqpoiaZKMf4t2/v7OflrqWQF4gQah7sA6AmUtmRjRDhTNRjPTMzFQQLkIsl9qvRCZtgl9EfgDcCnQYY6rsZVOBR4E5QDNwhzHmWLrakE5EhKKJRVTWVAaieuZcNScQ1ZNXYGnObW+2UX5JOX6/PxBzLgg7NuxIeXGPve1dPNNoFRvfc6wL5k0KrJtWXGDlsl9YBk/tZcLEIi67oHTYIXuo8O5u7wYYNsojmra9cOXCoG2jCZNkox9i3V9EAuGtdQ/W8co3XgGIOnt0uIlguS40R3qSkZI+0qnx/xCrgMuPXcu+BrxojPmWiHzN/v7VNLYhrVx292VBBc7d9ty6dXXs/NlOwBIkA10DDA0MkVeQR92DddbkmCSH/MYYdrV2WumNG1vZd6gnaH1J3xDnd5zk/PZeli+fT1FHPwPvvUvNfUvDRtmEO35oGgVnBm31qurA5C84U6hF0raNMYHJWA7DCZNk7b+x7u8If1+tLzBjNdLs0eHMOU5WTTe5JDSzYZKRkj7SJviNMa+KyJyQxbdhzQIG+BHwMjks+CF8LdDAUN/Alge20NvRC1jRPg7xloJzx3bvbDnOJl8bm3xt7D/SG7TdzMnjubjfz7RXPuSmFfO5Yt2yoNmg3a2Wxh6L3TlUeO/YsCOQJdOJ6hGRM9I9+P1+PB5PUDx44JhIUP7/bBImTqoCZ1QDVkGPcHVko5lz3AW/s7GfsZCsmU3JbjJt4y8zxrQCGGNaRaQ0w+ePSrSQsnjDzUSEmvtq2PqdrUwonUD/sf5AtM/ClQspmlgU849n28PbaTrWy4dV03mqbj+HXLNnAaYN+Lmo5xT3/v3VXFQ5mYb1DbxT3EGR/QN1frDO7M947M5uU4lbkDvbO8d0lm9/eDt7f7OX+bfOD2xft67udOz+xOwUJo7Qd2z6V/3jVQEbvzPKiST8w5lzRoPQHOlJRkr6yFrnrojcDdwNMGvWrLSfL5q9Foi7aML2h7fzztPvUDStiN5DvRgMR989StGkoqCC3pEY8hu2NR9lU2MrT+3v4JjfwOtdgfVTTvRzdfkkLi/Ip+3/NSBAz7wyWLM0MBPSnX/H3fZQQTUc4UY1EBxB1FLXgvEbDu0+xIn9JwDo6+yj8SeN9J/oZ/GXFmOMyVphImKNYpzZo05UD0BLfUvYF3U0G3i29jNeNMxydJJpwd8uIhW2tl8BdETa0BizHlgP1gSuZE88nDYf0V4bkiI4FhOJ3+/nnaff4f0X37eyYZZ5KSwp5HjzccYVj4uYf2RwyE/9e1bhkueb2jjcHZzLvrRrkPM7TjJr1xGmdQ5QXFpMjwg1riIloWGWDo7Qf/yOxwPhpnA66+JwNXfD4TZp1T1YR0+75WOYct4UWne0su+5fQCcc+M5Qfn+s1WYhPpsgKB8Rm4SsYFnSz8VJdOC/yngM8C37L9PZuKksaThjSU1QKwmEo/Hw/xb5wPQsbOD/q5+ikuLmXvdXObdMo/BnsHAkH/glJ839h1mU2Mrz+9q53hvcC776pmTuNnOZf/b5Y9YC8cXYIpOp01wtNOt39mK3++nuLQ40G53Cbsta0+XMHTSFjimjSX3LEnI2ex2iIKVWfFE8wmG+oeQPGFC6QRWPbUqKF9PNhOPI3g0mHOUsUk6wzk3Yjlyp4tIC1amz28Bj4nIXwAfYOX7TyvDRV9sf3g7A90DgWRlvo0+utu7A5OPAppfnKF5l3/+chbdtYjvLf5eQKA6U9/7Bk7x2rtHuO+xN3lhVztdfcG57C+d5RQuqeDsqROGDancvHYzGILyp29eu/l0sqtVVQAUTiwMpNp1au4CVNZUJpx10d02b5mXvqN9nOo7hTGGvHxrFrNTJWu0CcPRYs5Rxh7pjOpZFWHVdek6Zzhijb4wxiAI3e3d9B3to2hqUSAXOJBQURQnnE9EGPQID33rVd6bO5EXd3fQ48plLwIfmT3V0uyry6mYND7oONFCKv3GH/i+5EtLEJFAHHrR1CKrcIndTrdztqm2CREr6+Ltj92elNB32uY3fl7++ssYv0E8YqV/DkkGN9qEoppzlFwka527qSRa9IUTW17/YH1A4H/smx/DI57T9V2Nn12P7orZlhvIy/JYE/0fn8+7s0v4na+N/s4eeMuyg3sElp47jZurKrhpYRmlJUUR2z5cSOXBrQcBWPbl4Lq0Tg7+0PaFzqYNDVeM1eQTaNtKy9ex9aGt5BfmM3nuZGYsmMGB+gO0v9VO2cVlcVf4UhQlfYwJwR86mcYRzI7QXrZmGdu+sy1Q1NojHmruqwGxwhXf++17TJ47OSZb7omTg7y4u52NPb28ec1ZDHb1gM8S9nlA9fgCVi4/nxsWlDHNWxixzaGpnP1+f8SQSneRErfZxfke6ttwOyQfv+PxoHBFZ59oEUvh2vb77/0+EBHjZAZ18tycc/05XP75MxIEKooyQox6we+EVXYd7AqUIKy9rZatD20FCJh7JsyYwNDQECcPnzzt8Fy9hPoH6q0JRyurAscMteUe6xnghV3tPONr5Y13DzM45Mpln+fho/Oms7y6gusvLGXyhIJh2xzOGe2OhXe/bNxpmB2zlbfCy/xb5wclawsXXw5QuaSSlrqWwGza4SKWorXt9sduD1wfiBwRoyjKyDKqBb8TZ96xs4PSi0oDwrG7tZvSi0oZVzwuICwnz51M5ZJK/MbP1oe2UvegVZ/W4/EE8sm7Bdjh7gGe39XGpsY2trx3hCFXMvvCfA9Xnz+DFdUVXHtBKSVF4yK2LzTEFIg5s2Oo47rAW2AVUj/YFSjbB8Ejk1CHpCOcfbW+QM76SBFLwznKIXLMv6Io2UNchVhGimQKsbjNGw6OY9fj8dCwviFQXMTJpuk3ft741zcY6h+iaGoR9+6/F4/HQ9uJPp5rsvLibGs+GlS4ZEJBHtdcUMqKqgquPn8GxYXR36nRQkydAiehbR4uGZrz3Z06NxZ7vYmj0Emk6zkaHbeKkutIhEIso1rjh+FTywblkxFofKSRno6egNAfmj2Jr//Ly+yeXkTD/uBEoiWFVi77m6vKuWr+jDNy2UciFs051vDR0P6FmqFiEfrxRCwNdz0VRcl+Rr3gdwSbW/MNLQLuLK9ZXUPdA3V0nPLz4ZVn0fUH57Gz5QT0nLQ+wKTx47hxQRnLq8u54rzpFObHJuzdhAsx7T3UGzBHAYFkYR6PhwkzJgyb/thNrFkgE5l9qql6FSX3GdWC3xFSWx/aSulFpax8ciV16+pofKSR5pebmX/r/EC0ybvtXTz88FZeXjGbQ1Ps0MoWK+fMRBEWe4v409urWXbuNMblJT8LNVRz9vv9dB3sYsv9W4Jm1H7krz9imaHCCONkU+fGO/tUU/UqyuhgVAt+EStlcOlFpXS3dlO3ro6a1TU0v9xM+84OPFeezWsvvM0mXxvvOKl4baE/2SOcu7+Lm6vK+cyXr6BgXGovVajm7C3zWlW7Nvo4edgaXTgFQKzOnCmMU5E2IJ7Zp5qmQFFGB6PeuQun4/gbN/po8+bzdtkE3j9vEq1DIemNPcJtS2ezorqCS8+eTP0DdVHj2R3iTdkcTXPubu+muNTK23/39rtjmlQV7/mTJdPnUxQlMcasc9cYw1stJ/jdBZN5tKaUTq8dWmkL/VKPh6Ul4/mzT13CRTMnBiUTi8V0EUsCuFDCac7OSMQpzwjBtvPhTDbRvqcaTVOgKLnNqBT8fr+h4YNjPNPYynO+Ng6esBKXYQv9yZ0DXFVWwpLCArp+vofqVVVcXDkpboE2XHRONE3YbWJxJkF1HewKzBlQ27miKOki5wW/IzxPDfnZ+v5RNvnaeK6pjY6u/qDtpncP8rHSEpYUFNDys230H+2jzZXELNxx4czCI6E29mjpnIcT1m67emFJYVD8vdrOFUVJFzlt469/eDs7jvawf/4UnmloodMf3Jc5+XksKxlPTXER0/uHAsJ0w0c2BEIl72m+hx0bdpxhrnn8jscBApkro5lw4pkAFQ21nSuKkkpGjY2//9QQr+89zCZfK5uaO+gxBradLo5ddvgk18yawpLCAg49vtvSvr+wGAhOlRxagSqowparYImTuTJa2oRUxbWr7VxRlEyQExr/ZZddbv7vj59mU2MrL+7uoKs/uHBJ5fF+zm8/ydm7jzCx51RAqLtNLk6ytu7W7kDKhtrbaunY2cHiv14cJPyBM0ouhh4PokfnaBoDRVFGmpzW+He1dvL5nzQEvnsEFs+dyvKqCm5cUMavr/8JAKa4EPGezmvvNt0MdA8EcsM7ydo6D3RSelEpBSUFXHb3ZTTVNtF7qBe/30/NfVaN2KbaprAVuUDj2hVFyU1yQvD7jSHPIyw7dxo3V5Vz44JyZpQUBuXZN8ZYxb4lfC76Am8BZReX0XmgM1AOceLMicy7ZR6X3X1ZwFzjlC+sva2W2R+bHbYil1uga/k9RVFyjZwQ/JWTx7P9765nSvHpXPbuPPtOYrPX/vk1RISF9ywMSnNQs7omkJ6570QfU86dQk9HDyLC3t/s5b3fvseJ5hNBJqB9z+1j33P7mFA6gav+8apA9k7kzPBKtc0ripJLJJ90JgNMKS4IEvruPPslZ5Ww9L6lHKg/QEFJAZVXVFJYUsjSNUupWlVFYUkhHo+HJfcuYVzxOAa6Buho7KDvaB+DvYN0Hui0XhYrF7L0vqV4PB5WPrmSguICxhWPw1vmZdmaZUHHU8GuKEoukxPO3XDhnOHywruFt7ONOxSz8ZFGju07xtDgEP5BP558D3Ovm8vKJ1cGZse6twVLe3dXrFKhryhKrhDJuZsTGn843I5Uh2VrlgWlXHDb3Qu8BZScVcLU86YiCJ5xHvIK8ph3yzw8Hk+Q0Pdt9FH9yWru3n43Vauq8G30nRGyqSiKkqvkrOCPFD8fbgTjpFbobu3GW+Fl2gXTKJpSxNDgEHt/sxe/38rbEylKR008iqKMJnLCuRtKvHnhd2zYwYG6AxSXFwc5g3f+eCcdOzuoW1cX2EejdBRFGe3kpOB3a+Y1q2uCzD4F3oIz0h70d/VzvPk4k+dMpmpVVSBC56JPXwSEz3Mfej5FUZTRQs46d8EK6RzoHgho5E5cf4G3IFBZC07n4w9XIBxUsCuKMjoZdc5dv9/PQPdAwPHqCPetD23lnaffCdjtnfw8Bd6CoP1jyXOvKIoyGsk5U48xJpBNs2Z1DRgrJXLdA3V4PB5KLyql80BnwG6/5f4t7PzZTibOnBh0HC0QrijKWCWnBH/D+gb6O/sDydOMMWDg2HvHGOofomhqEfNWzGPvM3tpfKQR30Yffr+fob4hKxmbFjlRFEXJHcEfqHZV66NqZRULVy6k7oE6ejt6ASguK8Zb5mXvM3vpOthFT0cPxaXF9B7qDYwEQh3BGqKpKMpYZEQEv4g0A13AEHAqnPMhzD5B1a6MMaeFfmkx9+6/N+DAPXXyFCePnKTvqFVycc61c7jzV3cGJndpiKaiKGOZkXTuXmOMuSQWoe/gFv5OkrXi0mKKy4qpW1fHknuX4K3wMtAzwPhp45l+4XSKphbRdbCL+gfqgyZ3qdBXFGWskjOmHrAnbq3dEkiVPK54HGWXlDHnqjmWzd6AjBOKJheRNy4PsFI0eyu8Z8T3K4qijFVGSvAb4HkRMcDDxpj1oRuIyN3A3QCzZs06PVu31kdlTSUzF89k/6v7aX6p2cqueedCPqz7kNZtrQBWEfU1px25A90DWsNWURSFkRP8VxhjDopIKfCCiOwxxrzq3sB+GawHawJXaB4dgKVrlgbKJ3Yd7EJEqKyppHJJZaBgijpyFUVRghnxmbsi8k2g2xjzH5G2cc/cDdXa/X4/31v8vcD3u7bd5Rw3sEw1fUVRxiJZM3NXRIpFpMT5H7gR8EXfK2j/wP/OrFw34dInq9BXFEU5zUiYesqAX9rCOB94xBjzbLwHiTdDp6IoimKRccFvjHkPuDjZ40TKnQ9qz1cURYnGiNv4YyFSdk44036v9nxFURSLrLHxpxrNna8oihIfOS/4FUVRlPhQwa8oijLGyCnBH+qPyAX/hKIoSraRM4K/YX0DW+7fEhD2Tjhnw/qGEW6ZoihKbpEzgr+/qz9QZtEdw9/f1a+av6IoShzkTHZOdy5+Z5KWO4ZfURRFiY2c0fjdE7QcVOgriqLET84Ifse848Zt81cURVFiI2cEvzsvz13b7qJqVVWQzV9RFEWJjZyx8WteHkVRlNSQU7l6NC+PoihK7IyKXD2al0dRFCV5ckrwK4qiKMmjgl9RFGWMoYJfURRljJFTgj8XHNGKoijZTs4Ifk3KpiiKkhpyRvBrUjZFUZTUkBOC/9DuQ4FZu5qfR1EUJTlyQvA7qNBXFEVJnpwS/JqXR1EUJXlyQvDPuHCGJmVTFEVJETmTpE2TsimKoqSGnBH8TkZOFfqKoijJkROmHgcV+oqiKMmTU4JfURRFSR4V/IqiKGMMFfyKoihjDBX8iqIoY4ycKL0oIoeA/SPdjjQyHTg80o1II6O9f6B9HA2Mxv7NNsbMCF2YE4J/tCMi28PVxRwtjPb+gfZxNDDa++dGTT2KoihjDBX8iqIoYwwV/NnB+pFuQJoZ7f0D7eNoYLT3L4Da+BVFUcYYqvEriqKMMVTwK4qijDFU8GcYEWkWkUYReVNEttvLporICyKy1/47ZaTbGQ8i8gMR6RARn2tZxD6JyP8RkXdF5G0RuWlkWh0fEfr4TRE5YN/LN0VkhWtdTvVRRM4Wkd+JyG4RaRKRe+zlo+I+RunfqLmHcWGM0U8GP0AzMD1k2beBr9n/fw34t5FuZ5x9+hiwCPAN1ydgAfAWUAjMBfYBeSPdhwT7+E3gy2G2zbk+AhXAIvv/EuAdux+j4j5G6d+ouYfxfFTjzw5uA35k//8j4A9HrinxY4x5FTgasjhSn24Dao0x/caY94F3gcWZaGcyROhjJHKuj8aYVmPMDvv/LmA3MJNRch+j9C8SOdW/eFHBn3kM8LyINIjI3fayMmNMK1gPKFA6Yq1LHZH6NBP40LVdC9F/gNnOF0Vkp20KcswgOd1HEZkDXArUMwrvY0j/YBTew+FQwZ95rjDGLAKWA18QkY+NdIMyTLhqOrkaU/xfwLnAJUArsNZenrN9FBEv8AvgXmNMZ7RNwyzL+j6G6d+ou4exoII/wxhjDtp/O4BfYg0f20WkAsD+2zFyLUwZkfrUApzt2q4SOJjhtqUEY0y7MWbIGOMHNnDaFJCTfRSRcVhC8WfGmCfsxaPmPobr32i7h7Gigj+DiEixiJQ4/wM3Aj7gKeAz9mafAZ4cmRamlEh9egpYKSKFIjIXmAdsHYH2JY0jEG0+jnUvIQf7KFZd0+8Du40x97tWjYr7GKl/o+kexsVIe5fH0gc4BytS4C2gCfg7e/k04EVgr/136ki3Nc5+bcQaJg9iaUp/Ea1PwN9hRUm8DSwf6fYn0cefAI3ATixBUZGrfQSuxDJl7ATetD8rRst9jNK/UXMP4/loygZFUZQxhpp6FEVRxhgq+BVFUcYYKvgVRVHGGCr4FUVRxhgq+BVFUcYYKviVrEdEhlzZE98Uka+NdJsSwY4J/63dhzsT2P/vXNfAfU2+JCJbQrbNF5H2kDh1RQG0ApeSA4hItzHGO9LtSBYRqcHKbnlVHPvkGWOGwiwPXBMR8QD7gY8aY5rtZTcDXzHGXJeSxiujCtX4lZxERCbZedLPt79vFJG77P+7RWStiOwQkRdFZIa9/FwRedZOkPeaiFxgL/+hiDwkIptF5D0R+YS9vEJEXrW1ap+IfNRefqOIbLGP/7id/wUR+ZaI7LITfv1HSHtLgZ8Cl9jHO1dErhOR34tVn+EHIlJob9ssIl8XkdeB24e7FsZKN/A44B5FrMSadKYoZzLSM8j0o5/hPsAQp2dbvgncaS+/AdiCJeSedW1vgD+x//868F37/xeBefb/S4CX7P9/iCU4PVh52N+1l6/h9OzqPKw87tOBV4Fie/lX7XNMxZrh6YyiJ4fpx9XA0/b/RVjZH+fb33+MlTgMrJoNfzPMNekO+f4R4Pf2/4VYOXWmjPS90092fvITfWEoSgY5aYy5JHShMeYFEbkd+E/gYtcqP/Co/f9PgSdsrXwZ8LiVtgWwBKTDr4ylOe8SkTJ72TbgB3Zyr18ZY94UkauwXg5v2McpwHr5dAJ9wPdE5DfA08P06XzgfWPMO/b3HwFfAB6wvz8abqdIGGO2iYjXHgFdCNQZY47Fcwxl7KCCX8lZbNv2hcBJLI27JcKmBkubPx7uBWLT7z40WMVX7LTZtwA/EZF/B44BLxhjVoVpz2LgOqwRyBeBa6M1P8o6gJ5h1oej1j73haiZR4mC2viVXGY1ViWlVZzWzMF6rj9h//9J4HVj5V5/3x4hIBYXhx7QjYjMBjqMMRuwMjsuAuqAK0TkPHubCSIy3x5RTDLGPAPci5XfPRp7gDnOcYA/BV6JrdsR2Qh8CuuF81SSx1JGMarxK7nAeBF50/X9WeAHwOeAxcaYLhF5Ffh74BtY2vJCEWkATnDa6fknwH+JyN8D47A05LeinPdq4CsiMgh0A582xhwSkc8CGx1nrH3eLuBJESnC0uZXR+uQMaZPRP4My/SUj2VW+u9hr0T0Y+4SkV6gwRiTyIhBGSNoOKcy6hgt4Z+Kki7U1KMoijLGUI1fURRljKEav6IoyhhDBb+iKMoYQwW/oijKGEMFv6IoyhhDBb+iKMoY4/8HgRyfdLovEdcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.regplot(ads[\"TV\"],ads[\"sales\"],ci=None,scatter_kws={\"color\":\"purple\"},marker=\"x\")\n",
    "ax.set_title(\"Sales:  7.03259 + TV*0.04753\")\n",
    "ax.set_ylabel(\"# of Sales\")\n",
    "ax.set_xlabel(\"Expenses for TV\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will create a model with **sklearn** library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X = ads[[\"TV\"]]\n",
    "sm.add_constant(X)\n",
    "y = ads[\"sales\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "model = lr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.047536640433019764"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.032593549127693"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.611875050850071"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X,y) #R Squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([17.97077451,  9.14797405,  7.85022376, 14.23439457, 15.62721814,\n",
       "        7.44616232,  9.76595037, 12.74649773,  7.44140866, 16.53041431])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's predict a spesific value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.98332636])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[20]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.07666909, 7.98332636, 8.55376604])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[43],[20],[32]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>sales</td>      <th>  R-squared (uncentered):</th>      <td>   0.897</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.896</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   1733.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 19 Jul 2021</td> <th>  Prob (F-statistic):</th>          <td>3.52e-100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:16:40</td>     <th>  Log-Likelihood:    </th>          <td> -597.51</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th>          <td>   1197.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   199</td>      <th>  BIC:               </th>          <td>   1200.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th> <td>    0.0832</td> <td>    0.002</td> <td>   41.633</td> <td> 0.000</td> <td>    0.079</td> <td>    0.087</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>20.228</td> <th>  Durbin-Watson:     </th> <td>   1.707</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  23.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.846</td> <th>  Prob(JB):          </th> <td>6.36e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.086</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                  sales   R-squared (uncentered):                   0.897\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.896\n",
       "Method:                 Least Squares   F-statistic:                              1733.\n",
       "Date:                Mon, 19 Jul 2021   Prob (F-statistic):                   3.52e-100\n",
       "Time:                        07:16:40   Log-Likelihood:                         -597.51\n",
       "No. Observations:                 200   AIC:                                      1197.\n",
       "Df Residuals:                     199   BIC:                                      1200.\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "TV             0.0832      0.002     41.633      0.000       0.079       0.087\n",
       "==============================================================================\n",
       "Omnibus:                       20.228   Durbin-Watson:                   1.707\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               23.930\n",
       "Skew:                          -0.846   Prob(JB):                     6.36e-06\n",
       "Kurtosis:                       3.086   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr = sm.OLS(y,X) \n",
    "model = slr.fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.0395966795387"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_square = mean_squared_error(y,model.fittedvalues)\n",
    "mean_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.799957987268087"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_square)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "results = pd.DataFrame({\"Real\": y, \"Prediction\": model.predict(X),\n",
    "                        \"Residuals\": y -(model.predict(X)),\n",
    "                        \"Square of Residuals\": (y -(model.predict(X)))**2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Real</th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Residuals</th>\n",
       "      <th>Square of Residuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.1</td>\n",
       "      <td>19.155735</td>\n",
       "      <td>2.944265</td>\n",
       "      <td>8.668697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.4</td>\n",
       "      <td>3.704608</td>\n",
       "      <td>6.695392</td>\n",
       "      <td>44.828280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.3</td>\n",
       "      <td>1.431893</td>\n",
       "      <td>7.868107</td>\n",
       "      <td>61.907104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.5</td>\n",
       "      <td>12.612316</td>\n",
       "      <td>5.887684</td>\n",
       "      <td>34.664826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.9</td>\n",
       "      <td>15.051529</td>\n",
       "      <td>-2.151529</td>\n",
       "      <td>4.629078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.724272</td>\n",
       "      <td>6.475728</td>\n",
       "      <td>41.935058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.8</td>\n",
       "      <td>4.786853</td>\n",
       "      <td>7.013147</td>\n",
       "      <td>49.184238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.2</td>\n",
       "      <td>10.006603</td>\n",
       "      <td>3.193397</td>\n",
       "      <td>10.197785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.8</td>\n",
       "      <td>0.715947</td>\n",
       "      <td>4.084053</td>\n",
       "      <td>16.679492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.6</td>\n",
       "      <td>16.633272</td>\n",
       "      <td>-6.033272</td>\n",
       "      <td>36.400369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Real  Prediction  Residuals  Square of Residuals\n",
       "0  22.1   19.155735   2.944265             8.668697\n",
       "1  10.4    3.704608   6.695392            44.828280\n",
       "2   9.3    1.431893   7.868107            61.907104\n",
       "3  18.5   12.612316   5.887684            34.664826\n",
       "4  12.9   15.051529  -2.151529             4.629078\n",
       "5   7.2    0.724272   6.475728            41.935058\n",
       "6  11.8    4.786853   7.013147            49.184238\n",
       "7  13.2   10.006603   3.193397            10.197785\n",
       "8   4.8    0.715947   4.084053            16.679492\n",
       "9  10.6   16.633272  -6.033272            36.400369"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4607.91933590774"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(results[\"Square of Residuals\"]) # root mean_square error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.03959667953869"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(results[\"Square of Residuals\"]) # mean_square error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.944265\n",
       "1    6.695392\n",
       "2    7.868107\n",
       "3    5.887684\n",
       "4   -2.151529\n",
       "5    6.475728\n",
       "6    7.013147\n",
       "7    3.193397\n",
       "8    4.084053\n",
       "9   -6.033272\n",
       "dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.resid[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RPPJIfKyjFYjg8ccDe2l0VMccohpJLq8cR8DlufZa4Jhj0jew3BtOQwSmIpiYCP+O/KxG1fepqWTyWrcunJlWJrZvD8i1ReyhziMCmyLYsiWoKIODusKnVQR79+p9qySCZiiC//iPYLI3myLYuze+Ud+0KSBbmQFTtjXERGAqguXLgUMP1f8PDzce3xVPP61z4+MWmM8TIyiKCF7/euAjH9Hv9+7VjWRUTIfvty1G8NBD+u/hh9OdXyqCrq70ioBThyUR2OqFhIsiuPpq4LzzksuSF9PTumOyZIn+v0VGF3ceEUhFMDioK+OePUEl6ulpbDBM2IhgdFRbD7291VlDaRRBViJYv14/jFNTjYpgYkJ/nqQIuOFNSwR5gsXDw42/6xNP6LIsX67/P+CAxuO7YssWfV/ibDG+1z092Yigtzc/EWzcqBv+6engnkfZQ3wuGxHwPZKxMReYMQKXcSBSEXCDLusLlzOqLrvECLgNKHsN85ERXU+YCLwiqAhSEQD6oZyYCCpKd7cmibTWUKsogrKtIV67Vt6z7dv1A8S9tKhjKhVNBHHlMBWBfKjzxAieeEJPNWISQRYi54YprufJx124sBpFoJS+F+YgvigiiFIEExPBM/Db36YrQ94YAf/2NkUQ9bu5KAJbJ6MMcKfpwAP1K9fH//xP4Mgj3UfLF4zOIwK+8SYRFKUI5MP6zDPNXUyD00c5a6gMRcBTMch7Nj2t7ZckIuBtDjtM/99Ma8hUBHv2aL962bJiFAFfezOIIOvkvJyqOzoabkhdiUAGi7MqgrwxAj5v0dYQn6Oo53XfPvtvzNdvKoJf/lKnct97bzHnT4nOI4I9e3SF5kptEkF3dzIR2MYRsCJga2jvXj2M/tpry7kOG9Iogizpo/v2BYFGqQgAbYnwwxnVqHODk9UaSkMEMoNEBov5nvD5Bgc1ASxeDLzgBY3Hd4WLIuDj5iEC/p+xbl2YGMbHo8ca8DlNIpApvRJxMQK+lgcfdBvdLcvAqbBFKYIka8glWFz0fEaXXgqccUbj51GKgAc2PvBAMedPic4kglmzgv9Na6inJ5s1ZCqCnTv1Z3Iys7JhBovjUtP4AUyjCDZsCN6Pj4cbzO3bkxUBByUPPFA3ArInGkUEchoJ1/TR1auBI44IpvywEYFs5Go1bRO9//2Nx3eFS0PC59xvv2zjCJgI+P5u3apJVS4f+YUv6NXzbJBEIDsJaa0hSQTT0+HBeEkYGdHxAaLiFUErWUMbN4afF0YUETz9tH71RNAkjI4GDSWQTRG4xAi48sp5bsoGB4vTWENpFAHHB4CAPHluJqkIoo7J962vT5OtnC4higj27QsCeK7BYm7Ytm7VDcCePY3WkNnIDQ4G15IlRuBqDfX06IYw7RQTNiLYtEl/941vBNs+8kj0NBRMmnv2ZLOGJBFMTgYDMu+80/1aeHoJoHhFENWIuwSLi7aGJift9Yg7AJ2mCIjoKSJ6gIjuI6KGrgNpfIGI1hDRH4johFIL5KIIXIlA/tBm1hBX1DSyOS/YGmLrq+gYgY0I9t9f/y8VQVSjbgbkZdpiFHlIZWYqgjlz7A2qbJT5/scpAkatpnuqZVlDPEo3LRFEKQLuXf7mN8G8/my32bJfslpD5jgC7jgtWqTttDTz+OzYkZ4IZqIimJiwl4d/MxkjmJoKRnivXp09BpQDzVIEr1BKHa+UWmn57kwAh9f/LgRwZaklMRVBd3ejIkhrDXEFlYqgCiLgYDGQTGZZsoZMIpiYCIjARRGYRCAbqyjykPfPJIL99rNbQ3KWUv7ehQiAoGOQFi6KYGJCH3/OHH3+pOm3GZOTej8uK99faS9997v6lYnAVg6bNbT//tmDxd3dekqRX//afQba3buD8RpZFIEkAm4wXWIErsHiomIEsqwSO3fqzgY/N6Oj2kKanAROOEE7CGwTNRGtYA2dA+DrSuO3AOYS0QGlnc2mCKTkzJI1xBVVxgiqsIZYEQDJ11C0IkhLBEND4e+yEMGCBfaetSQC3n9oqFwiSKMIuP655pBHKQImgoULgeuv1++5UZ+a0vfm9tuD4zApSiI45JBGIuBpm5PGEfT0AGefrc91yy3prgXINo6A69D0dKMyj8samp6O72k3yxri65cJHWwLnX22fq3AHmoGESgAtxLR3UR0oeX7AwHIma/W1z8LgYguJKJVRLRqS54ZGJNiBFmIQKakVmUNTU7qys6KwMyZt20PpFME5ipeExO6d9ff3xgstj10puqScCECU75LIpia0gsL8ToH/Jn5u46N6fsURwTj49rumDfPnchdYwS9vW5ZXRJJ1tC55+op0KemAkUwOQl87WvAaacF6pbvlZx6/ZBDgsQGxm23Af/v/yUHi3t6gBe/WBPRj3/sdi1TU5oAgGwji2VDbXY84gaU8bmjUIY1NDHR+Bzw9Uv7lhVAmxPBS5VSJ0BbQO8lolOM78myT0MropS6Wim1Uim1cuHChdlLExUjyGMN2RRBs4mAG1JXRZAla2j9ei1rgUARsNUxMmL3bSVMawgIbIIoQrIpAj7Ofvvpz8bGgB/8AHjta4F77gkrAr7OWi2cTRVFBL29+toef1z3uF07Ha7po0USASuC44/X2zz8cHBdHCTnVyCsnni21UMO0a9SFUxP631ciKBWA846Sy/q5NKoT02FFYFtn507gSefDP63BYvl5y4xAt4/CmUoAtvxJBEODur7zIrg2GP178FL3jYRpROBUmpD/XUzgBsAnGhssh7AUvH/QQAseVcFIUoRmNbQ2FjyZFw2RWBaQ3mJwHV2Qi6La4wga9bQQQfp95w+2t2tG3M5oCzquDYimDdPk0vWGAGgG7g77tDvt29PJoLR0WRriK/FdS6YNFlDeYmAy759u7a8eICeHNwlr53LJomAA/U8mE6O6Ziast8jM1jMMYMTT9RlcSHNycmgIYwigk9+Ejj11OB/W7BYXpcrEcT9NmXECIBkImBraNEi3ZF805u0zdbktalLJQIimkVEw/wewBkAVhub3QjgHfXsoZMAjCilItIYCoCLIkjKw09SBNIayhMjWLNGN7IuUtGmCFzWI3BVBJOTutfIDYdUBDYisDXsUYqgvz8bEbAy3Lw56EXt3h1ulCURyLRa0/9mcH3g8rgSQZoYQZGKYO5cPToaCE/3YCMCGVhnRbBokX6V95lVRFKwmP/nY7gsoykbwlpNWyemfbJ5c5hU5O9pUwRJto6LIijDGgIany95/bNmBdYQP1fveIcu53XXFVMOR5StCBYBuIOI7gfwewA/UUrdTEQXEdFF9W1uArAWwBoAXwHw16WWyEURJAXz5I/MQ/aBIEaQZA2ddx7wrW/p969+NXDFFfbzrF+vKw6nBsaBK3JZ1tDWrdoyYEXA5JlXEQwN6TJnIQLuNX73u8EyoXv25FcEMqOn1Ylg3jzg4IP1/zyAjsth5tzbFAGTqeywTE2FZ5GNs4aAfETAn0nwuZkgmqkIiraG4ohAKgIm82OP1dlDX/96MeVwRKlEoJRaq5Q6rv53jFLqivrnVymlrqq/V0qp9yqlDlVKPV8plWKYYgakUQRRjYCsLLL3z4pAqeDBshHBzTcHg3BWrwYefdR+nqRsCAmuyGVZQxxc5Ic+SREkEQFnDQ0N6TInxQiIGntthx0GnHyyXraQjy0HS8nG0JUIOEaQlghc00fzEIGZPrp9u1YEAwP6d1ktxLaLNUQU2GuynnJwlbe3rUeQlQjMrCEuqwSTAAdbbVlD8rpksHjTJj3VtlwjwyVYXBURbN+uiYCnXQGAt71Nr6HNsYMmoBXSR5sHbrxcYgRAsiIAdCU0YwRA8BBNTDQ2cjKbZWIiORbhUjnTKoK01hDniUsiiFMEcdaQVF2zZjVaQzffHARCuSHm3Hs+Nx/nLW8JX2eUIjBT9pJiBFwe18baVREUESMwrSFAWwsyFTPKGpo3T7/fulUTCAfrTWsICBrTKhQBoO+prNfyuZHbyXqxapXOeLrnnsbrMa2hkRG9JoVS9plt88DFGhoc1JPMTU4Cxx0XbHPkkfo1bvW4gtFZRMCNilQE3d1hyZnGGgLCNhBbQ0C492WqAnm+OCJI41uaweKk9NGkAWW7d4d9WiYCHjfA5FmENSSJYPdunYVyzTXB/4OD+npsRHDeeToNkbNfirCGylIENmvoF7/QjXhSllqcNQQE1gJDzt0vFcHixfr9c89lJwIzWMxxnqKIgO/93r1B2VlByg6DTRHw91IRRFlD//zP2l6UdbXZWUMct+EJD4GA3NOMPs+JziICcwpqIN4aWr0aOP/8xt5tlCJgawgIj/o0icBUBFGDarJYQ0UpgksuAc48M/jfZg1xY1BkjGB0VPfQ2MPevVtvIxf8kUSwaJGeLO6979XbxAWLmxEjSJp0ziSC++7TwcK4jJu4cQRSEUhExQh4qu3nngsIlqgxRsDHB8Iz9XJ5JBEQ6d/BlQhMa8is/1IR8H1lwpJ1zBYj4HsjRzpHKYIdO3SZ5fOdlQjMEe6u1hCgf1e5bjZPwZFmYsKc6CwisCmCOGvoG9/QIzZlTjMQrwhMawgIE4G5ulfZ1lDUaMqkGIGZuWFaQzwwixXBnj3hRjOtIuDt+aHkh2D3br2tJAL5WwHA5z4H/N3fBeM/8iiCZscIuAGOs+g4FiPTR3nJQyYCUxFExQh4jpuJiYAEhobsMYLt2/V944YryhoC3IlApo92dQVllZBE4KII5HNiUwRRMYKpKb2vvHb5rN15p30GURNPPKGVmZyF1dUaAvTaz3xvgYAIvCIoCWkVAf+wZk9ifDxoyExFYLOGZG+LK2UaInDx8W3BYjkM30RS1pAkRyB4sDjLhB9CVgRAeBI51/RR0xpiQuD7x4pALvgzMaEbkS6j+g4NxROBTB8t2hpKEyPg30gSQVzQ3qYIdu7UJG8qAj62SQRjY/r8rAiA4H4MD0dbQ/L+mLOPZiGCNNaQqyKwWUM2RWAjAiBIpeVrY7zhDcCnP518TRs36mdNrt8cpQgkEXI7I20hwFtDpcNVEfD3/JCaRDAxEfRQ0lpDXPmYAFgh2JBXEQDR9lCSIjAzNLZt0z0V2YgBgSIAtIIwM1ts54wjApsisFlDsgfFYEWQNI5g375qYwRdXUEMJysRMDGbMQKe3ti0hrhR4RgBENyPoSG7NbRjR+PsrFwe8zfIYw1FKQLXGIHNGrLFCMzn2EYE8lkbHXVrjPm4chCYS4yAnwGTCGbN0tt4a6gkpFUEDLOimkSwd69+SLu6kq0hWUHkBFo25E0fBZKJII0imD8/6AXaFMHmzUHDNDam/e81axrPWauF00dljMCVCGRvlDFrlt4+ShHwvZFEYB6n7Kwhuf5BWiLgso6NBfeHe48rVuj3z39+UA6pCLg+LlzY2CM1rSGpCLg+A9pGkrP1mopgy5bkSeTMkcXyfIysikD+blkVgXzWJifdRvbbiCCNNWQSAVH0FOslobOIIE4RyACkSQQuioD3kdYQb2PrbclzlqEIkhanScoakuUD9IM1b17w8EcpAm6Y9u0DLrgA+MAHwsfkOf+TYgSmNeRKBHHWkEkEPT3B3EmMMgeUcYwAyEYERMGARZMIBgd10PmCC4J9JBFwMHPOnODeR1lD3JibigAIZ9mZRDA1FW5UbUibPmoqgqRgsRkjkMQUpQikpSnrPA+sSwIfVyoil2Dx/Pm682gSAeCJoFTEKQJpW6Qlgr17g4dKKgL206OsoTKIoEpFMD0dNExjY7pRMBUB2wKHHKKDZMcfb48RZFUEcQPKurv1HxOB2cgB5QaLmXyAbEQABLES0xoC9JoLfPwoRcDrMgDJ1tDUlJ0IohQBkGwP5Y0RmNaQHANgyxoyx1ZIcN2IihFMTrqtGRFnDcURwQUX6LUcbJNozpnjraHSEKUIzHEEchQnYCcCOWumVARygXEbEfCx5DmLGEdgSx8Fio0RJCkCIGwN7dypR0fywygbtHnzdHru859vjxFww7Vnjz1YbCOCuGAxn5fPFUUEWWIEcvRrmdYQENwHUxEwZONqixHMmRPUjSRrCLATAZc1iQgefhh46UvDJJM0sljWO1uMgP+v1fR7+WzYsobksdNYQ0rpepvXGoqLEQwPAyedZD9m2lXscqKziMBVEZjbyAaRg7tJigDQPWiiZEWQFCNwyRpKGyy2ZQ1t3hw8yKYi2LZNXw9bOzZFAAQNE9sRY2PBCEnZCEjYiGD3br19WkWwY0dQbtMa4nPxNNRZiOD++/W0DLy0IBDMOcXnjEJRRDA+7kYENkUgiSApawiwE4H87Rk2Irj9dj0ZoEy/TlIE5uj0KEUwe3Z4OnHAnjVkkoxEHBGYqbdxMIlgejp4pm2KwPYMmPDWUImIGlmslH4QZUri0FAwilZWIK4oSTECQH82a1ZzYgT8AEjrAUhWBLyw9+c+pwOO558fnJN7RbyQCff2e3ujFQE3TNu2BY0jNwRRRCB7+7JBfPZZ/SDOmeNOBNLvjSKCJEUgGxTz/t18s742uZygtA+ifkueOyctESgVbQ11dYXvvbxOkwjiYgSmNSQ7JrasKlci4Pd8f7g+mUQQtWRpXIyAiUDeN2kN7drVmJqdRhGYg/HiIGMEZhZgnDUUh7lzvTVUGkZHdW9W9tql1SEr9pe+FMwKOjmpve4jjgh6gi6KgIfwR2UNmUTwlrfoEb2MNEQwOakfWg5+Jk2lLcntt7/VA7L27QsaUiltuXc+f77+jFdW4veyMRoe1g2UlMlJRMCNs7RYAOCRR/TrkiWNA8qiiMAM9qUlgqQYwV13BfeFIe9xFBFwqnDaGIFpbUlraM6cxrEUSdZQVIxg9267qrEpAvnbM+bO1dvaiIDvDzf4fC22AWWuimB4OJz9BYR/N0DfI5dgsS1GkEURjI9rwpV1IysReEVQInjmUZkpIoOfspE65xxg5Ur9fnJS+52PPw48+KD+LEoRmEQQ5b/aiOCBB8IzSKYhgqmpcKPgag0BwTTXy5Y1nnNyMvBbJRHwA8LBdb6nPJZCEgHPohhHBNxjlg/yQw/p1yVLAkuEyxZFBOY1mkQwMJAvRsCDDOVv4qIIzHELg4O6XvDxXdZ55v1ZEZi2kNzOZg0NDISz4vh1eLhxiU+GqzVEpAeryeVMTUUgA/fyVZ7PDAbbFAFnncUpAkArt2YqAkDXe5uDIM/pSgQ7d7qt6VwAOosIzLUIgGhFADSOpAQCuRalCOSD098fnZFhixFMTTV6nrxtEswKlpQ+KisrTyWx336ND8LkZOC3sjVkKgKepoDP29cXnp7CRREA4dkmgWCkJisCl2CxRFZriLNRgDARbNkSWEJRiiBqriEbEUiyTCICvl6pCGTGEEMO+jKtIZ66wKYIgKCeygZTdmyAaCIAgKVLw7ETkwjM38IlRmAbUNbTo8suYwTd3WFLD9Bk2cwYAaCvuQhFMHdueDr7ktFZRPClL4XTGYEwEZiNlI0IuHecVRHEWUNTU/ZK5EIE0nsF3GMEQGAHLVjQmO3gogiAQLoPDOjGlomguztMBLYGXI6YlQ8yE8EBB7iPLJbISgSyXJII2Bbia2G4KAK+n5II5EPuqggkEdgUQVywePZs/b8tRgAE9TQuRhBHBAcfHF5EiZMETKURlzVkEsHoqL5mLgcrdyYCvm/Dw4GS421dFQHX766ubIpAHtdUBHmsIaBp9lDZS1UuJaL/JKKHiehBIvpbyzanEtEIEd1X//toaQUyA5tA2BqKUgSygWZFIOcaShMjiLOGohSBS9aQaQ25DigDworAZg0lKQIgTATSGjrqqHSKwCSCnh5dLtdgsXmNWWIEjP3202Xm80oiyGoNmcF8RloiiLKG4mIEpiKQ6aNAUE/jrKGoYDGgFcH69QGR5LWG9u3TSmb27OD6oxTB0FBgDfF8SqYiiCICxtBQY4xArpQWhThryBMBAGASwP9QSj0PwEkA3ktER1u2+5VS6vj63+UllykMFyKwWUMDA/oHjcsaYmvINX3UVARpxhGYFaynJ9xgmzAVAZFuWIpQBLIn/YIXaLuA72EaInj2WW0L8YjaLERgNj4u6aMMXr2L7+GqVUFZ0waLbdaQRBZFEGcN2RRBlDXEv53NGnINFgNaEUxM6N9tdDSo91mtob17dZkkEUQpAiaCffsCIti2zS1YzJDJBnLbpFX84qyhrDECJvkmZQ6VvVTlRqXUPfX3uwA8DODAMs+ZGlHBYiCeCHhaYCYCfqi6u4OeOVtD5oAaIJynzxVS9j6BcMO3ZYu2buQC5RK2CjY4GD0ganIyUC9btuiHzQzI8msWRcA4/nhdNiYDlxiBVG38UPf16YeaybIsRSCPy8TH9/Chh4K5fGyKgFfRsqFIIuBxBGmsIZcYgas1xNdr/pZLl+rXZ54JZw+lIYI8ioCtIZ5YL40i6O8PEgWAcOOeZA/xtr29XhEkgYiWA3ghgN9Zvj6ZiO4nop8S0TER+19IRKuIaNWWuEU80iIuWGzOtgiEF+vo69MPz9RU+MHmhrC/P701FBUjWLdOB7Vuv91+HWaMANAPEJPQlVfq1bBkObjMzz0XTE8QFSyWs7Ka4wiAxhgB46ij9OumTfHjCIAgRjA4GByP58+Xs5pmCRbzeV2yhhisCPbs0cd55hmdQgzYFcGsWeliBBJR9p+NCHbt0vc/rTXkGiPIkjUEaEUA6LoqiYC3N6/FNo5Ajhzet09f6/BwtCKwWUNDQ3ofM0YQpwj6+4PpM8zvXIlgyZLiiKCdFAGDiIYA/AeADyiljKV8cA+AZUqp4wB8EcAPbcdQSl2tlFqplFq50DY3R1aYFcz2XZwikFYRgx8eqQjMHG3bFBNxRMA9Rs6tN2HGCAD94PNAossvB7761eC7ycmgMWJFwETAqZy8HctzThGVhBGnCHhAHV+DqzXU3x88CCYRjI/HjyNg9PbmVwSSCDZs0OfllaRsimBoqPwYgczVT5s+GhUjSGsNxcUIAE2Ycr3dLNbQvHl2RcAWrs0ampoKgsvz56dTBH19+RXBkiXFZQ21myIgoh5oEviWUuoH5vdKqZ1Kqd319zcB6CGiBWWX60/Imj7KRMAKwaYIBgb0wzo9HfS2bHMNuaSPcoV/9FH7ddgqmCSCkZHwsSURbN2qt+3tbVxBje0q2SDI+xQVI+Dz87bj49HZPqY11N8fPAhsDUkicLGGhofDjSGTZNpgMaCJgNNGbUQgUxyzWENy9lUTNkXADatr+ui+fbr+uVpDScHiqCm8583Tv4GpCKKIwDagjLedOzeIEUhFsG9foAhklplMge3v12UxYwQu1pBp1wLuRLBwYXiKEyDfOAKgPYiAiAjA/wXwsFLqsxHbLK5vByI6sV6mrWWWKwTX9FEza8hUBFHWkLnakK2CpVUEtiyGKGto5059HHPwjbSGpqfDjbb0aZkI5ENve2+zhphc+FqKUgQuRMCNsvngSSIwc+TNa+MYwehoMCjusMP0a1ZFYCOCBQvSEQHDNUbAYGvo4IP18XhaCEkESoXrl20cAcP8DYi0KpAxgsHBRiJwSR+NUgSsBpnEuKPD17Bnj/6Nue6nsYbyKIKuroDQi7CG+vr08drEGnopgLcDeKVIDz2LiC4ioovq27wZwGoiuh/AFwCcr1RSvlaByJo1xESwfr3+X/rTUhGYC1HLyidnyeTXqPRROauinE+HYbOGeJg6k5CpCGTDKYlAZhpxLCOrIkhDBNzD6+sL7hsTgYwjuBAB+/VRRDA2lmwNLagL0z173Igga4xg/vz4dSGAbERgNnx8T087TddbXs2sr08ff9euoAct40EScUQAaJJhRTB/fjAnEJAufZSJwMwa4jIwEZgDPPl6uHefxhqSMYK0RMAryHFHhZGVCICmTjPhMA1ediil7gBACdt8CcCXyixHLLgyj43FEwFzE/8wTAQbNuj3p54a7CdjBAyusLKC2YggSREAWhWYcZI4a4jLLI8hrSHelsstiYDVUJQiKIIIeHtpDXEjF2UNxQ0oGxjQ30cRAaAbmDQxgqef1j1ovk5bQzFrVvTCLKadIm2ZtNYQwzV9lMFEQBSoAf6f05x5n+Fhfd1ZiOD++/XrokX63qSNEdRq+vxPPaX/l9YQn5cbfr7fkgg48GsSQZnBYiaCohQBECaCv/973Qb967+67ZsSnTWy2AZbo8awZQ3J/fihPOusoNEAwtaQ6fXZFEFUjECOIzCJwEScNWRTBGamU5QiSCICfn/66XrSvAMOyG4NcaMh71saa4hXIRsc1O9tRMANsK2Rk+cBwkTw1FN6gXguv6kIuCFIaw0ND4dnXzVhm2KCkdYa4ntqg0kEbCOlJYKlS7UaeOghTQScpcVlkseIUgT9/eHR6TZFwGptw4ag/Azu3adVBDJGEDVy3IY4IsgaIwDCMb477gDuvNNtvwzwRBDVwAHBtNQ2md3bGzyUb3tb+DszWAy4W0MuisAWMI7KGtqzJxgHEBUs5m35oZdjD1wVwbHHAt/+dpgg5THjsoZk1gpbQ4cfrkmFfXoXIgB0r5wH+8UpAnlMiShF8NRTelI+In08M1jc3x+c04Y0RPCRj+jpUID0MQKZNWSz/6IwPBy2hqKIIO55AfRCK11delQ4dwrSjCzmUfr9/UEihkkEPT0BEfAkd1GKwHVAmRkjyKIIeD6soqwhnqEW0HXQZWBpRngiiFME/JmMEcj9uOd69tnh76Q1ZAaLXawhtqFsRHDAAYEi2LgR+P3vg31tigAI4hhJ1lCUIjAzbKLiBQxurHgdAb6WJCIYGQl6hO97H/DYY0HKKh/ThQhYEfDvlpUImIR27dIB0OXLg21MRcDkY9YThpluG0cEN9wA/PCH+r0tfZRfpfXIMLOGZAOZRhHwb5JWEZxxhu6lX3MN8NGPBiO5gXhraNMm3cGRRMCwWUNsjTIRyEGIMgMozYCyImIEUhH09+cjglmzgmdxdLRUIig1RjAjkNTDkQ2Kud8ll+ievqy0gN0aSlIEZs9FpunJaZGPPTaYOO9TnwKuv14/eLYKxufmGSGzWEMcLE5SBLbrd7WG2BMeGQliBLVauBFzGVAGBKuZSUUgz5mGCIaH9f9r1+ryRxFB0YpgfDzoDUcpgrlzw9OpM0xraHg4yOCJIwLufeYlAkBbQhdcoN9LRRBlDU1PAx/7GPDznwMvfrHeR5KczRpiIuBOjpm152oNcfn4vuYlAjk7wOBgcYpgdLSxnSkQXhGYFcz2fRQRvOxljWoACFtDZhpYVIxAfs5LH8qsIW4oFiwI9uMRpnyMKEXARMDH4LECpm2QJVhsawzSxgiAIDDGisCEy4AyIL81JD/r69PHe+AB/f+yZfo1ThEUQQRyWo84IrDBJAJJpnHWkAyuy22zEIGEjBHEWUMjI9p+273bTRHMmaNfOegvLbM0wWK+zjzjCLizwWVga3VwUP+WV16pY2i8bRpFwMfy1lDJSGrU4oggCmbWkFyIOsoakhVvYkL/zxaRtIaGh8MEIW0lW4wACKYG5v14H7PXlSVYnFYRRN232bMDIrDl9/Nx9u3TpBd1nFNPBU45JTlYLI8pwcft7dX3c2gIuPde/RlPL5FFEZjpo3z8KEWQRAS2jCGgMX2UiaBWaxzNbO4nPXVeptVUEWmJwKYIbAPKuNF+6qkgWMywKQKiIE7Q29tI4LYYgfnbTE4G15d3HEEUEYyPa/uWZ67Nowi8NVQiXGMEtqyhKHCFkF65TRFI79RUBOYSfJz3LieGGxsLE4GrIuBK3tsbXJ/MULDFCNIogqiRxUmKgBe7j1ME/JBF3f9PfUq/nn56QJRZYgS83RVXaGvo9NODMQQmEfzxj3qiMxdFwMcnCs+pZCoCXvIwryLg48+ZY7eSGGYP+ogjgF/+Ejj55PB2Sc+LCdcYAd/PNWuAP/uzeGuIy7BggY6TmUSQRRH09ely5g0WA+G10cfG9G8plUYaImAlYGuDCoQngjIUAdtB/ODJhahdiEAqgIGB4P++vnDvUSoCF2vIVARceZkIbAu2cwW0BYiJGlUIELaGurr0eVyIYOvWaGvI7G0l9UbzZg3xdu94h30b+VA++WSwrKmrNQQAn/0s8KIXAdde26gIgPB0Ba5EQKT/TGsoLj7Ax5f1sKsLePnL7dsxsioCW/ooX6dMH2UMD4d/Q96f4wSc+inP6RojMK2hIhXBwIC2rkZGshEBB4v5eN4aKhFlEYHs0ciBITZryBYj4MaA50hny0TOy+9qDfEcMqYiqNXsvXfXYHHUPZDH5O1ciWB6Ot4a4ociqTdaFBHYwHUC0L29rVuBFSvSxQgA4N3vBk44wW4NAdoeSmsN8bamNRQXHwACa8jstduOzSgyRiAbOVuMgNN25XmZCOKsoSQiYII0xxFkUQT82/DzxjECVndK2TtsURgc1PuwTeiJoERktYZsjQjj3e8GPvOZ4P8oRWD2vBkTE2EiAHQDyETA3meSNWT2AvmYsnHh68gSI4hqjE0iYPKKI4LZs4PsljhriB+yshQBfxZHBLLnyKuvHXJI0ADbMDERbszMc5pWIKAbAHOKCS5flCIAwo16WkXAnnpRROBqDcn6LxXBrFnBdnxumyLIaw3FpY+6DCiTnSppDY2P606gUo1EmAQ5OzDgiaBUZFUEcT/mi14UpM8BYSKwKQKgcY4hkwh279aVVebT8zbT0/aexqxZYV+YlYeU6Hy84WH3AWW8XVRD8F/+ix5kx2sR9PYGyiJOEfB588QIGGUqAkkEa9fq1yRFsGNHdGPMi+5MToYbjO3b01tDQPjaXYkgrSLgwZZJYGuIM9XkMeIUgbl6mtxPxggA/TvK+uAaLJ6a0mNF+vv1sWzWUHd3/mCxOcVLGmsI8ETQFGQZUNbTEx94MyGtoShFYPYI5YIbgCYC2fORRGBr8IAgK0VCDnip1fTxuNcVN6AsjSI4+GDgG98IGlMXRSAbKhdryJUIzAFlrllDaYngkEPiiWDz5iATx4Q5oR5DWkPmFBMuRMDZVb29ydaQ2YOOauSTbEET/f0BycVZQ1GKQJY7ShHkCRZzVtgFF9hHFg8PZw8WMxFwEkZaImiiIvDB4lpNN+pKRQ8oM3ssrg8BY+7cYIplVyLgB1HOsy6JYGwsqFhMBLaHV2YD8Xlkb6evL+zlm+WKCxa73gceep9kDTGKDhZnGVCWxhqaM0d79kUQgZx4d9s2e+PX1RUMbrOhVgvqU3e3LlvSYk5msDhJEbj+9ky8su6bRDA9HR0jiFMEaa0h+V5aYKxcZYyAX4eG8ikCOUgvqyLg2YY5pTxNJ9QRngiAIJiZpAj4h01LBHLiuShryJyfhH9saQ0NDdmtoShFAAQNLJddxhW4F2MOHsobIzDR2xu9zi1DKgJbI8znLNsaShsjWLtWqwF5Thu2bAkWtTEhiUA+5Nu2BdNc8H1bulSTD68GZoMkgloN+PGP47fn7aSVUhQRyAkFXbKGeJ+0isBmDbESYdhIwcxGMhVBFiKQwWJ5zryKAGjslBUEbw0B8ZVbEgE3VlkUAaCJwFYxgGhrSBJBnDUUlY3AZebeqFQEHOCKUwRZsoZM9PY2rnEcVU7A3ggT6fPlJQJp6xWlCFasCJ/TtpyGqyKQ9cCWNQRo6y2uV1irhRudlSvD007bUJYikIsOmdaQHFAWFSOwEYGrIgAaJ1pk2K6zpyeIZfC2aawhmyKQ4M5QXiIoAc1YqvI1RPQoEa0hoost3xMRfaH+/R+I6ISyy9SAuIatSCLYsSM5vZDf84PMRMBL8GWxhoDgoZExgu5u3ZuVQV0gHCxmUsqrCNIQgS1GwMfJSwRE4diFbT+5jQ1MBNPTmgikIgDCAUpAl+G554ojgiR0d6fvfZrB4qgYQR4iSMoa4rqaZA3xa9LIYqBxVT7zvS0LSg7ky2oNcfxNIq81xGUrAaVaQ0RUA/BlAK8CsB7AXUR0o1LqIbHZmQAOr//9GYAr66/NQ1zDJtNHuUHPag2Z65lKmIqAe5WmIshqDUlFwA1VdzfwzW8G57IpAq68ZSuCpBgBoBsF7h1lJQI+/t690RK7p8dtHMGmTfr+mERgnpPXzo3y6SURyOvKSgRmjMAFpqce1Vil/e1dYgRMBCtW6EVtkqwhPjdPEy6toVpNb2cSARMdI0oRAOH7MDwcpAhHwRYslv8zirKGSkDZiuBEAGuUUmuVUuMArgNwjrHNOQC+rjR+C2AuER1QcrnCaJYiMK0hCTNGYFpDPLI4rTVkEoGZNSRHB9uIgHtDtmBxWYogqhE+/PBgLYY8RMCNU1YiYEXAczgdfHBwTqDxN+YHOa0isKWPusCMEbgg7TiCImIEchT0xARwzDHAm96k54qKs4bk6/z54ZHFfD6TCMxFg6JiBEAxisATQQgHAlgn/l9f/yztNuXCRRFMTgY98rTBmrTWkC1GADRmDZmKwMUaMrOGJGzBYn4I0owjMFGUNXTUUe4NY5Ii4HLZsGJF0Mu3gYmAr4ntiygi2LxZv6ZJH+3uDhRB1FQeUTBjBK77pBlH4EpMcTECfs+KYNYs4PvfB57/fLesIQB44xuDSQbloC6TCHggJiNOEfCkj3z+NCuUAZoIOG1XIm2MoF2sIdjXKzYjaS7bgIguBHAhABzMPbCi4KIIeDDUrFnFZQ1JmDECrizmEnzSGjJjBC7BYlYWfG0SNkXA74uKEUTdOxdriGMZccdh5CGCe++Nb3iZCPjB5uMlEYGLNcT1YNGigAjSqAEuRxZrSAaLi4oRxFlD/J4VgSzr7NnAJz8JvPnN8ef+yleC91LJ2YggSRHYYgQuwWJOUZa/I69lIZFVEcj07xmqCNYDkHlrBwHYkGEbKKWuVkqtVEqtXJiUE50WroogKxEMDele3c6dutLYMj6SBpQB4WCx7GG5WEM2RWAbgNbVFTTaRPFE4HofenqChyluWgq5oI8NRx7ZWIYoRA0ok8ePIoLu7nKIII0iWLxYE0FUWnMcslhDZaePSiKQ1yN/J3lMIuDii4MZX+V+Ufejt7cYIpDKaHBQH8NMAJAwFQGfP681xLMDS8xQIrgLwOFEtIKIegGcD+BGY5sbAbyjnj10EoARpdTGkssVhmuMoKcnGxFw6iNPEmdr6JJiBEDYGuJcZSBeEbz85Xr5QLY64qwhINx7HxiwW0NlxAiA8NzwNjRLESQhLRFs2aLrAAc3TUQpgunp8KAyV2TJGurudpsPJ8vIYkDXoyhriAeUJV1nEgnJBWqyWENmjKBWCyuaKJjBYi5jXiIAGlNQZyIRKKUmAfwNgFsAPAzgu0qpB4noIiK6qL7ZTQDWAlgD4CsA/rrMMlkR17CZ0jULEQDhaRZsDZ1rjIAr+q5dwedxMYKVK4FbbgnPe2/rmTHktUkiyBssNidPs0HOBGnD8uXu8YmokcVAcrA4CVFEwOexKYIFC6IbAHMZTkArAt43bX3LEiMwG85mpI/ye5siiDt3VB2Ks4ZcgsWmNdTdHdSVOHuIt+3uDtR+EYoAaCQCc+nLglB2jABKqZugG3v52VXivQLw3rLLEQvXAWXd3XpN1axEwEGo7u7GUahmjICRVxHI8/Ox43p98trkzJF500cZeRRBraYzhx58sLUUATcWcdZQnJ0ZpQgAPSNrM2IEXPYkS6mIGIEsU1eXPUaQ5dw2RSCn/04bLO7uDs/1FaXouG0g0ufft89OBGmDxUD4+eeylYDSiWBGII01dOWV2c8hH7KenqCRMkdW2sYRAGEiMBVB0jznsrFJsoZkGYsKFjPi9pGrRUXhqKOKIYKurnQPpESWGEFUfACwjw1hb3zNmvglJm3Imj7KZYjbr8iRxfyeYxN5FUGRMQK2hrjx37o1WLPahIxDMRH09OQPFgPtYQ3NGLgOKEvbM5NgRcC9BzPvOWkaaiDaGuLgVlyAUyoCF2uop0d/X0SwOI0i6O2Nnz5BThAWhyQiyDNfC9cJVksuMQIXIpDW0NFH69cdO7Ipgizpo1yGuP2KGEcg66ksa15FkDdrSMYIWBFIIoiCzOySisS0MfMQASsrTwQlwlUR5CECGSzm3jZgJwKOEdRqYZtENmC2GEFaRRBnDXGPJm4cQdGKYM6c+IFcAPDa1wInnZQ8m2atFgQ/iyYCVgR79wZLcfI5Af1bfP/7wW+UxRqaNw846CD9vlnpo0CwX1HTUPO1sTXU1RUm+lotUFZ5FcEhhwRJEbYYweQkcPXVwF/9lVuMoFYLprGQufwmZNsgnw1+zxMHZiEC7giybeqJoES4po9miQ0wOFhsEoG0BRisCMy1WONiBEnWkC1GEGcNsSKICxYXrQhe9SrgDW+IP9bJJwN33hlvHwHBvZiYaLwvhx8OHHpo/P5x4Ovesye8NjWfZ9064M//HPje9/T5t293JwJ+0Ht6gnTZZqSPmg1nUYqA521iIjCvpUhF8KMfAV/8YnhbUxH8/OfATTfZO0O2YHFaIuDfUmYNsarIEiNgRcCDUj0RlIg0A8rynMNmDXHFMWME4+P26XXTZg0xZGaKizXEWRBFpY8y4vb5i78AvvY1t2MmQVod5oP3D/8A3H139mPz9fNEgOY5+bfZuzeYJC9uYRjbaPHeXuCII/T7ZqSPlhUsBoKkA5s6k4rAlQiitpOrpnH5+Hp6evT5R0fDI4fjgsW1mlZmROmJQD7jTAR5rCFPBE1AmgFlWRGlCOJiBDYikI0Qo8isIVMRNDNGUCRkw2ZeJ89xkxVJRMD3jKfwlvvYQBQs3sPb9/bmUwRyYkEXlBUsBsKKwEYEtokN855bKgJODJia0oQURQS8j3zemQwkETz6KPD00/o9LxMbFyPIQwRNsoZ81hCQrAjGx7XfXIQicI0RTE7aiYAHp6W1hmq1wDKIs4bMGIE5M6l8X7QiKBJxRJAXrkQwPu5GBID+baUi6OnJrgjMrJw0+xQdIwACIhgYKMYacrkfkghqNf3/1FQ6RcDHWLAgHCx+5zt1eu+PftSorm3WEMcI8lhDngiagLjKnSaYFQdz3V5uYOOCxba1WAFd2dKOI+D9XK0hVgTm5+Y2LqiaCIo+ZxIRsJ0mBwa6EkFRisD2Pg5pYwRpyjQwEKSPVqEIuBPEmV4ycy4pWAxoIpCKYPfu4PkzO1UyWMxTyxxYn0MzzzgCHyxuAuIqt2y0i0gf5Qbb7EHYppiwKQI+lpk+qlTyDJWSjKKuR1Zk+X2ekcXyGppNBEpVpwikNZSUpWRTBMuW2eebSUIeIigzRmDrrHR1FRcjsG0riSDJGrIFi4FGIpic1AkB/F6eT1pDCxcCd9wBvP3tQVlcy8/wMYImIskasr3Pcg6XGEF3d1gRyHNGEQFXDldF4Jo+2g6KwHxfBPgadu0K8rvledLGCICgsZREUKvpgWV5iMB13zKDxVKJxllDZSgCtgY51jc6Gj3dtowRSNKyEcHOnfovigi4jC95STA6OU+w2FtDTUBSsNjcLgvMAWVR1tDAQFgREAWVmCtZX5+ejIzhmiooA9bmtTHSEEGWGEGee5gGZRIBX8Pu3cnWkCsRDA4GvdXu7iCYfckl6ctfhDVU1FxDQLgDUkTWUN5gMX9unjNKEey3nyYCpfTvwo3/unXBVCA2RWArC1+zK3ywuImoQhHwg26OI+jvDxSB7F1IIuAGncGVI8kaMhVB0jgCW+MP6PK/7nU6p98F7aYIoqwhvrYsimBwUO/HHQDG296WvnzyHhedPpolWNzXF6zOV0SMIGuwmBUBYPfr44LFY2N631mzwkTAGUFRisAsi3nOJHhrqIlwVQRlpI+aMQJWBGNjjUPUoyZLS6sIslhD5jlvvBE4++z489n2bSci2Ls3OUbgGixmIpiYyK+aslhDVaaPlq0IzBgBkEwEZrAYCOwhSQRxwWKzLC2cNeSJAHBXBHke0KgBZaY1xIpgx47gx+fKJa0hiTREkHZAmfl5FrQrEQDx1pBMH00KFkcpgixoRtZQlTGCNIqAYwS1mr6/bMnaGmUzRiAVARAQAT8/NiKwWUN8XVkUAU8fP2+efvVEUCKqVARRMYLt24P8Y1M9mA1FmmBx2ikmGJ4IAiQRQR5raGKiGiJwHUcwa5Ze01muFpeEpBhBUVNM2LblEffd3eGU6zSKgO0fUxE880xysFh+l4UIXvlK4POf1/NrdXX5GEGpaEaMwHX2UVYE27YFvYAkIkh6eGUZJBHYtncJFqeFJ4J0MYIiraGiFUFPD7B2bbrySEVgIwJ5bJcyplEEfA7zvC5EwM+bizXEx7EpAv4uCxH09QF/+7dB+WYaERDRpwG8DsA4gCcAXKCU2mHZ7ikAuwBMAZhUSq0sq0yROPdcLedt876nqahxiEofNYPFAwPApk26PFFEkNUa6uvTjY0ZsDbLya9RweK08ERQnSIoOn00C5ImnWOUoQj4HOax0waLgWB0cVyMwEYEROFYSNZ7WyIRlGkN/QzAsUqpFwB4DMAlMdu+Qil1fCUkAOi53z/+cXvDWLQiSLKG+vuDxc6lNSSnO85qDZmjm6O2AcIxApnOmAVVE0HR55THKzpYXIQiyJI15BoszoI4a0iq0qoVAU9aZ1pDc+fqz22KwFyCNWop1e7ubIpAYiYSgVLq1vqaxQDwWwAHlXWuUlF0+miSNTQwEIwRkIqA5xkCsltDcQ+kLCe/ZgkM2lA1EZSpCFwHlLkGi2W2WFaUOY4gC1ytoaS68ZKX6LRlc/lGG7IoArOsvE+tFkw8x2tczJ6tj/Hss+Hz2RQBoOtMJxKBgb8E8NOI7xSAW4nobiK6MOoARHQhEa0iolVbtmwppZBWFKkIpqeD+fGjrCHZw2QikGuxyn0YWQaUJSkCTwTRcM0aSmsNAXpsQhXpo2VbQ5OT9nmf0livp5yi05ZdyibPY1uWNIoIBgbsU2bPnq1HEvOsritW6NcnnwyfL4oIWlwR5Hoqieg2AIstX12qlPpRfZtLAUwC+FbEYV6qlNpARPsD+BkRPaKU+qW5kVLqagBXA8DKlStVnnKnQpHpo4CuZEnjCBjSGpKNf9ZxBFIRRDUQZSgCczBaM9AKMYI0s48yEezYEb92gQvyKAJXdZkGXHdHRxtXoCvLwstiDQFBPMN8RnhQJ9tCvPQo20VxWUP8fdz4HRe0KhEopU6P+56I3gngbACnKaWsjbdSakP9dTMR3QDgRAANRFAZilQEgCaCOGtINvimNWQei3svriOLZYwgyRqSZSxKEUQFqMtAKxBBFkUwMhIEJ7Mib/pokSQAhInAtHWKSsYwwUti8qSDrtYQz5RqqmY5DggIxviMjOhXF0XAaEEiKM0aIqLXAPgwgNcrpUYjtplFRMP8HsAZAFaXVaZMKIMIooLFXV3h3n4UEfB7Vg9ZBpSlsYbyetZp1zguAq1iDaUJFgNaEVSdPlr0/eL6umdPvhhBWkiPP40isFlD3KNnIuApH0wiiKrrRajimUgEAL4EYBja7rmPiK4CACJaQkQ31bdZBOAOIrofwO8B/EQpdXOJZUqPoq2hffuiiaBWCze6XNl6esINjlQEvC+QbkBZM62hTiECvj72kdMGiwHtQ1eRPmqOxC0SfI/27MmXPpoWkghcFUFUqmuUItixI3yuOGuI0YJEUNqTqZQ6LOLzDQDOqr9fC+C4sspQCIpWBLzSmS1GIAlizpygwpx0UpCdII/FjUdaRVBV1lC7E4F5nizWkFLVpI9Ka6gsRTA6mm9AWVrwfbAFi1m1uQaLWRHw9BJtZg35kcVJKKrHYkpDmyLo7w8aTbaFAOCyy8LH4spmEoFL+qjrOAI5oKwoIij6QY9DmUSQNI6AkYUIgOrTR6siglaxhvbssQeLpSKIsobKJgI563CB8HMNJaGMaRakVOWGxBxoJokg6lhZYgSAfgiSrCE5oKwIz1oOiGsGymxgsiiCpN9GBlGriBE0QxHIRd4ZaQaUpUUWa0gGi+NiBIOD+vsoIjCvpYNjBO2BIgeUyeOY1hAQjhFw6qgNpjWUZtI5wB60M8tZZLCYj9GJ1hCPFE7KlipDEXDmjAv4t4mzDbMi7h61oiJgayhOEfT06JXHzBhBlA1qjmvIAk8EFaLoGAFgH0dgfh6nCHgfM/XUJX0U0BK9mQPK+LjtSAS2kcUMHkfg0rBLIihKEaS57iKsiyiYdVyiWTGCvMFiUxF0d2sVl9YaynNvPRFUiLKIYOlSXZF4ilv+3BYjiDpWb6/eJ03WEBBPBGUEi7ms7UIEcjyEizXkcv/KUARprltuW9Y4AiA6a4io+PPaFAH/blGL4UQFi01FwERgHoeDyLxOsa0sWcHzlZUATwRJKDp9lI95zjnAhg1B0AkIKwIXaygtEUhF0MwBZXzudiECoDHYbztP1USQ5n6XqQhcrKEyEgn4mDJGwAu9cANuko+rIqjVwo09X8ehhwI/+xnw2tdGlyXP9XgiqAhlKQIiPZWA2WC5KAI5HXWtlm7NYkDnqntrKB+KJgJ5nKLSR7MqgiqsoTLqhk0R8PQdPJ7HRFpFYJ4LAE4/PXocgSeCGYoygsWyMhAFclX2wsuwho49Vr9u2JDOGioiWCyP1wxUQQQmEctgcRKIAlVQtTVUJhFEWUNlKIIsRNDfHzxPSTECqQiS6rYnghmOMtJHo1Lo0mYNZSGChQvjt/WKwA22YL/pc09OpltohomgqGBxmvvNc/Hz+yJRtSKQwWK5CHwUEZj7A+kUQVxZPBHMUJQ1oMx2jrRZQ6Y1lFTJurqA007T75s5jgBoPyJg9RaXBQPoeIzr/atSEeTZLwlVxQjiFIGtLEB0FphXBB2OsmIEEvIBPOYY4Kij9GvSsUxF4NKTO/10/Rp1LQsX6uMsXtw+weIyzmvO/2Q7L5CNCKpIHwWKaaxsiFMEXGfLjhGYwWJbWYBkRcBTTKRVBC0eLG7ikzlDIStTWcs1Smvo0EOBhx92O1ZaawgIFEHUtgcdBDzzDLBkCXDrrfqzIohgwYJyeuZRaIY1ZCMCGVcZH9eD96pSBGkb17IUQSvFCAYGggY1ryJoI2vIE0ES+AfM22NxtYZcwA0FW0NpVj5avlwrDpm2auLAA/VrkcHia65p3loEQHVEwOcaGtJLjo6OhlND41AUEWRtdKSnXiS4IY5bqrJZWUODg0E+ft4YgS191KUsWeGJoEIURQSu1pALuIeVxRoCdE/f1oiZKDJGwCs6NQvNIALZezTPNWtWQARxpCvRrtYQEEzmVkWMQAaLWRHIc5vlNPfn91GKQAbak8riiWCGogxFECWP0yqCLNYQoEc1u6BIImg2mkEENoUjFQHgg8WMvr749QiaqQjiiCDKGopTBC5lLypGMD2t/wpWbT5YnISiGsM4RSBjBGmOlZUIXFFksLjZqNoa4qBklcHitI1rmYqAVWxV1lARikCp8BgDVgQuZS9KEQClqIIyl6q8jIj+WF+d7D4iOitiu9cQ0aNEtIaILi6rPJnRiopgv/10o7Fsmd6HV8Qq+gH2iiAa/f12719aQ8DMVARFxwiAZCJodrDYVhb+3iybLB8vaJNWEbQ4EZRtDX1OKfWvUV8SUQ3AlwG8CsB6AHcR0Y1KqYdKLpc7iuqxcNYRL6ZtO4drJZk7V69YNmsW8A//EHxe1qRdRQSLm42yieATn4g/LzcSY2M+fRQIetpVWEMyRpBkDcUpAiA8a2kbKYKqYwQnAlhTX7ISRHQdgHMAtA4R8GjRvA8nUbBUZF4iAIKGpswGzyuCaLzkJfHnlamFaUcWV5U+WqU11CqKIC5rCAgrgjREUFSMAJhZ1lAdf0NEfyCia4jINlT2QADrxP/r6581gIguJKJVRLRqy5YtZZQ1GnKUbR7IUbsSaWMEEmU2eD5GkP28MrWw2Yoga4NehTXUrAFlCxbo/5cty2YNmYqgzayhXL84Ed1GRKstf+cAuBLAoQCOB7ARwGdsh7B8pmznUkpdrZRaqZRauZDny2kWiiIC7ukVoQjMfYHyrKGZSATyXlQxojkPEVQVI2iGNVSVIjjgAGDTJuDUU/MpAkkE3hrSUEqd7rIdEX0FwI8tX60HIHMZDwKwIU+ZSgHPK5MXURWwKCIo+gEeGtINqmsefKuhVrMPYir7nMDMJIKy00eB6tJHgWARqKIVgcv9amciiAMRHaCU2lj/9w0AVls2uwvA4US0AsAfAZwP4C/KKlNmFK0I8mYN2fbNun8cFi4Efvc74AUvKPa4zcJMJYJOTB8te0CZRJRFC0RPh2GLEUQFwOPK0mlEAOBTRHQ8tNXzFID3AAARLQHw70qps5RSk0T0NwBuAVADcI1S6sESy5QNRccI8o4jkCjTGgKAlSuLP2azUGYPN+mckghce/jHHQcccYT7gL+kMmS1hqpIH22GImDEKYJazT4XkU0RMHG3QbC4NCJQSr094vMNAM4S/98E4KayylEIirKGyo4RNLPBmwmoggi4UZBZQ6515/jjgUcfzV+GVrSGktJHy44RSCQ1ygMDurFNUgRdXZoM2kAR+JHFLmhla0geyxNBGK2iCJodbM86CLKdRxZLJBGBjbRsigDQhO+JoENQtjXkFUE56FQiaEVFUPU4AgkXRWB+byoC+Tt7IugQ1Grlpo+2coxgJqNKIshiDRVdhlaMEVQ1slgiryKo1YIJB10VQZExAp7vqED4lsMFRaePzpSsoZmOVlEEzZ6iw48j0MhrDUUpAvn8tokiqHqKiZmB+fPj1xB2hQ8WNxetQgRVKYK0vewqrKFmjSyWcLWG4hQB49xz3RZcKoIInv98YNUq4LDDsh8jAp4IXPDd74bzi7Oi7BiBt4bCqNX0PaliZbRWIIJWUgQzKUZgs4bkyGL5+Yc/nK8saTA0BLzoRdn3j4EnAhcsWVLMcaKyhoqIERA1t8GbCajVmq+S2oEIyuhQVDH7aFSDnyVYLBVBlrLGDWJrAfguZDNRpiLwtlAjqiKCrq7wnDUzJX20yqyhVg8WR8UI0palRZ9TTwTNRJkxAm8LNaIqIujrCzcWPlg8M62hIhWBJwKPP6HMuYZatIJViqqIoLdX23RVzd7ayuMIqpx0jpElWOwVgUdhKHOuoRatYJWiSkUAVLeeQyvHCGaSIojKGsoTI2jR59QTQTNRpjXUohWsUnQ6EfgpJvRr1hiBbRyBPG6WsrToc9qaIex2RZkDynyMoBFVEMGRRwLbtun3TPwzRRGU2ak48USdc3/MMfZztpIiiBtHYL7PW5YWgSeCZsIrguaiCiK47LLgPTc4PlgMLFoE3HBD4+etOKCsAxWB70Y2Ez5G0FzUatXmbVdlDfH0yHIsgwuqUJetqAiOPhpYsSJYb0Aey3yftywtAq8Imokys4a8NdSIKhSBRFVEAAC33w4cemi6faporFoxa+iNb9R/tn3kcdOgxYPFpREBEV0P4Mj6v3MB7FBKHW/Z7ikAuwBMAZhUSq0sq0yVI2l0pVcExaKTiSDLynJVEEGZ9llSsDhNg+4VQTYopf4rvyeizwAYidn8FUqp58oqS8vgHe8ADj44vEA24ImgLHQyEWRBFeryqKOAz34WOOus5G3TIqsisEH+hlnqVKcSAYOICMB5AF5Z9rlaHosXA+ef3/i5jxGUg1YhgmYHi7OiisaKCPjgB8s5dpFE0OaKoBnU/3IAzyqlHo/4XgG4lYjuJqILow5CRBcS0SoiWrVly5ZSCloZfIygHFRNBFWlj2ZFu3UqiiQConzxjHaOERDRbQAWW766VCn1o/r7twD4TsxhXqqU2kBE+wP4GRE9opT6pbmRUupqAFcDwMqVK1WecrccvDVUDvr6ipk+PCtmmjXU4r3W1Fi6VJPxsmXhz7M2yt3dwNRUWyqCXESglDo97nsi6gbwRgAvijnGhvrrZiK6AcCJABqIoK3hiaAcXH45sHt3deefqUTQLupy2TJgbKzx86xE0NOjj+eJIDVOB/CIUmq97UsimgWgSym1q/7+DACXl1ym1kMRMYJ2eXiLxAteUO35ZxoRdEqnIo8ikK9Z9m3Re1t263E+DFuIiJYQ0U31fxcBuIOI7gfwewA/UUrdXHKZWg9eEbQnfLC4NRE1wj8JeRaXGRoCXvYy4IUvTL9vE1CqIlBKvcvy2QYAZ9XfrwVwXJllmBHwRNCe8IqgNVGFIqjVgF/9Kv1+TYL3E1oB3hpqT7T48oQNaLcYQRTyxAiAmfN7pkCb/+IzBHnS0jpFzs9E9PTo32emrCXdKXWpCkXQ4vBE0Arw1lB7ord35thCQOfUJa8IGuCJoBXgiaA90dMzcwLFgFcESfCKwKNU+BhBe+Lww4HDDqu6FO7olLqUVxG0IVG2+S8+Q+AVQXviQx8CVq2quhTu6BRFwJM+ph113saKoP2uaCbCE4FHK6BTiGDxYuD664FXvzrdfm0cI2i/K5qJ8JPOebQCOqlTcd556fdpY0XgW49WgJ+G2qMV0CnjCLKijRWB/8VbAd4a8mgF+LoUD68IPEqFJwKPVkCnxAiywisCj1LhYwQerQBPBPHwisCjVPgYgUcrgAe/tWFDVwi8IvAoFd4a8mgFvPjFwL/9G3DqqVWXpDXRxoqg/a5oJsJbQx6tgFoNeP/7qy5F68IrAo9S4RWBh0fro40VQS4iIKI/J6IHiWiaiFYa311CRGuI6FEisg7hI6L5RPQzInq8/jovT3lmLHyMwMOj9eEVQSRWQy9OH1psnoiOhl6m8hgArwHwv4nI1lJdDODnSqnDAfy8/n/nIe/KR4C3hjw8ykYbZ1Xlaj2UUg8rpR61fHUOgOuUUmNKqScBrAFwYsR219bfXwvg3DzlmbE480zgk58Ejjgi/b5eEXh4NAdeEaTGgQDWif/X1z8zsUgptREA6q/7Rx2QiC4kolVEtGrLli2FFrZyzJ0LXHxxtl69JwIPj+agjWMEiVdERLcBWGz56lKl1I+idrN8ptIUrGFnpa4GcDUArFy5Mtex2gqeCDw8moM2VgSJV6SUOj3DcdcDWCr+PwjABst2zxLRAUqpjUR0AIDNGc7V2fAxAg+P5qCNFUFZrceNAM4noj4iWgHgcAC/j9junfX37wQQpTA8ouAVgYdHc9DGiiBv+ugbiGg9gJMB/ISIbgEApdSDAL4L4CEANwN4r1Jqqr7Pv4tU038G8CoiehzAq+r/e6SBJwIPj+agjRVBritSSt0A4IaI764AcIXl8/8u3m8FcFqeMnQ8vDXk4dEceEXg0bLwisDDozloY0XgiWCmwxOBh0dz4BWBR8vCLy/o4dEceEXg0bLwisDDoznwisCjZeGJwMOjOfBzDXm0LDwReHg0B14ReLQsfPqoh0dz8LznAUuXAgfapk2b2Wg/aus0tLFc9fBoKbzwhcAzz1RdilLgu5EzHcPDwBVXAG94Q9Ul8fDwmKHwimCmgwj4x3+suhQeHh4zGF4ReHh4eHQ4PBF4eHh4dDg8EXh4eHh0ODwReHh4eHQ4PBF4eHh4dDg8EXh4eHh0ODwReHh4eHQ4PBF4eHh4dDhIKVV1GVKDiLYAeDrj7gsAPFdgcYpCq5YLaN2y+XKlQ6uWC2jdsrVbuZYppRaaH85IIsgDIlqllFpZdTlMtGq5gNYtmy9XOrRquYDWLVunlMtbQx4eHh4dDk8EHh4eHh2OTiSCq6suQARatVxA65bNlysdWrVcQOuWrSPK1XExAg8PDw+PMDpREXh4eHh4CHgi8PDw8OhwdBQRENFriOhRIlpDRBdXWI6lRPSfRPQwET1IRH9b//wyIvojEd1X/zurgrI9RUQP1M+/qv7ZfCL6GRE9Xn+d1+QyHSnuyX1EtJOIPlDV/SKia4hoMxGtFp9F3iMiuqRe5x4lolc3uVyfJqJHiOgPRHQDEc2tf76ciPaKe3dVk8sV+dtVfL+uF2V6iojuq3/ezPsV1T6UV8eUUh3xB6AG4AkAhwDoBXA/gKMrKssBAE6ovx8G8BiAowFcBuBDFd+npwAsMD77FICL6+8vBvAvFf+OmwAsq+p+ATgFwAkAVifdo/rvej+APgAr6nWw1sRynQGgu/7+X0S5lsvtKrhf1t+u6vtlfP8ZAB+t4H5FtQ+l1bFOUgQnAlijlFqrlBoHcB2Ac6ooiFJqo1Lqnvr7XQAeBnBgFWVxxDkArq2/vxbAudUVBacBeEIplXVkeW4opX4JYJvxcdQ9OgfAdUqpMaXUkwDWQNfFppRLKXWrUmqy/u9vARxUxrnTlisGld4vBhERgPMAfKeMc8chpn0orY51EhEcCGCd+H89WqDxJaLlAF4I4Hf1j/6mLuOvabYFU4cCcCsR3U1EF9Y/W6SU2gjoSgpg/wrKxTgf4Yez6vvFiLpHrVTv/hLAT8X/K4joXiK6nYheXkF5bL9dq9yvlwN4Vin1uPis6ffLaB9Kq2OdRARk+azS3FkiGgLwHwA+oJTaCeBKAIcCOB7ARmhp2my8VCl1AoAzAbyXiE6poAxWEFEvgNcD+F79o1a4X0loiXpHRJcCmATwrfpHGwEcrJR6IYC/A/BtIprdxCJF/XYtcb8AvAXhDkfT75elfYjc1PJZqnvWSUSwHsBS8f9BADZUVBYQUQ/0j/wtpdQPAEAp9axSakopNQ3gKyhJEsdBKbWh/roZwA31MjxLRAfUy30AgM3NLlcdZwK4Ryn1bL2Mld8vgah7VHm9I6J3AjgbwFtV3VSu2whb6+/vhvaVj2hWmWJ+u1a4X90A3gjgev6s2ffL1j6gxDrWSURwF4DDiWhFvWd5PoAbqyhI3X/8vwAeVkp9Vnx+gNjsDQBWm/uWXK5ZRDTM76EDjauh79M765u9E8CPmlkugVAvrer7ZSDqHt0I4Hwi6iOiFQAOB/D7ZhWKiF4D4MMAXq+UGhWfLySiWv39IfVyrW1iuaJ+u0rvVx2nA3hEKbWeP2jm/YpqH1BmHWtGFLxV/gCcBR2BfwLApRWW42XQ0u0PAO6r/50F4BsAHqh/fiOAA5pcrkOgsw/uB/Ag3yMA+wH4OYDH66/zK7hngwC2ApgjPqvkfkGT0UYAE9C9sf8Wd48AXFqvc48COLPJ5VoD7R9zPbuqvu2b6r/x/QDuAfC6Jpcr8rer8n7VP/8agIuMbZt5v6Lah9LqmJ9iwsPDw6PD0UnWkIeHh4eHBZ4IPDw8PDocngg8PDw8OhyeCDw8PDw6HJ4IPDw8PDocngg8PDw8OhyeCDw8PDw6HP8/DjrEPYaQ5OgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.resid,c=\"r\")\n",
    "plt.title(\"Plot of Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Multiple linear regression is used to estimate the relationship between two or more independent variables and one dependent variable. You can use multiple linear regression when you want to know:\n",
    "\n",
    "1. How strong the relationship is between two or more independent variables and one dependent variable (e.g. how rainfall, temperature, and amount of fertilizer added affect crop growth).\n",
    "2. The value of the dependent variable at a certain value of the independent variables (e.g. the expected yield of a crop at certain levels of rainfall, temperature, and fertilizer addition).\n",
    "\n",
    "**Note:** In multiple linear regression, it is possible that some of the independent variables are actually correlated with one another, so it is important to check these before developing the regression model. If two independent variables are too highly correlated (r2 > ~0.6), then only one of them should be used in the regression model.\n",
    "\n",
    "**Example**\n",
    "\n",
    "You are a public health researcher interested in social factors that influence heart disease. You survey 500 towns and gather data on the percentage of people in each town who smoke, the percentage of people in each town who bike to work, and the percentage of people in each town who have heart disease.\n",
    "\n",
    "Because you have two independent variables and one dependent variable, and all your variables are quantitative, you can use multiple linear regression to analyze the relationship between them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For a real world example, we will use *advertising* dataset.\n",
    "\n",
    "It can be downloaded from here: https://www.kaggle.com/ashydv/advertising-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We will understand the dataset first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper  sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3    9.3\n",
       "3  151.5   41.3       58.5   18.5\n",
       "4  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ads = pd.read_csv(\"advertising.csv\")\n",
    "ads = ads[['TV', 'radio', 'newspaper', 'sales']]\n",
    "ads.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ads.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's select all independent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper\n",
       "0  230.1   37.8       69.2\n",
       "1   44.5   39.3       45.1\n",
       "2   17.2   45.9       69.3\n",
       "3  151.5   41.3       58.5\n",
       "4  180.8   10.8       58.4"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = ads.drop(\"sales\",axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will select dependent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.1\n",
       "1    10.4\n",
       "2     9.3\n",
       "3    18.5\n",
       "4    12.9\n",
       "Name: sales, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = ads[\"sales\"]\n",
    "y[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will split our dataset as train and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ads.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_train = X.iloc[:160]\n",
    "X_test = X.iloc[160:]\n",
    "y_train = y[:160]\n",
    "y_test = y[160:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape:  (160, 3)\n",
      "X_test Shape:  (40, 3)\n",
      "y_train Shape:  (160,)\n",
      "y_test Shape:  (40,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train Shape: \",X_train.shape)\n",
    "print(\"X_test Shape: \",X_test.shape)\n",
    "print(\"y_train Shape: \",y_train.shape)\n",
    "print(\"y_test Shape: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "First we will create our model with **statsmodel**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Rsquare is explanation rate. Results means we explain 0.98 of sales variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>sales</td>      <th>  R-squared (uncentered):</th>      <td>   0.982</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.982</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   2856.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 19 Jul 2021</td> <th>  Prob (F-statistic):</th>          <td>1.07e-136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:16:40</td>     <th>  Log-Likelihood:    </th>          <td> -338.90</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   160</td>      <th>  AIC:               </th>          <td>   683.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   157</td>      <th>  BIC:               </th>          <td>   693.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0550</td> <td>    0.002</td> <td>   36.244</td> <td> 0.000</td> <td>    0.052</td> <td>    0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>radio</th>     <td>    0.2146</td> <td>    0.011</td> <td>   20.027</td> <td> 0.000</td> <td>    0.193</td> <td>    0.236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>newspaper</th> <td>    0.0173</td> <td>    0.007</td> <td>    2.331</td> <td> 0.021</td> <td>    0.003</td> <td>    0.032</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 5.571</td> <th>  Durbin-Watson:     </th> <td>   2.106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.062</td> <th>  Jarque-Bera (JB):  </th> <td>   6.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.272</td> <th>  Prob(JB):          </th> <td>  0.0485</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.782</td> <th>  Cond. No.          </th> <td>    12.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                  sales   R-squared (uncentered):                   0.982\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.982\n",
       "Method:                 Least Squares   F-statistic:                              2856.\n",
       "Date:                Mon, 19 Jul 2021   Prob (F-statistic):                   1.07e-136\n",
       "Time:                        07:16:40   Log-Likelihood:                         -338.90\n",
       "No. Observations:                 160   AIC:                                      683.8\n",
       "Df Residuals:                     157   BIC:                                      693.0\n",
       "Df Model:                           3                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "TV             0.0550      0.002     36.244      0.000       0.052       0.058\n",
       "radio          0.2146      0.011     20.027      0.000       0.193       0.236\n",
       "newspaper      0.0173      0.007      2.331      0.021       0.003       0.032\n",
       "==============================================================================\n",
       "Omnibus:                        5.571   Durbin-Watson:                   2.106\n",
       "Prob(Omnibus):                  0.062   Jarque-Bera (JB):                6.050\n",
       "Skew:                          -0.272   Prob(JB):                       0.0485\n",
       "Kurtosis:                       3.782   Cond. No.                         12.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr = sm.OLS(y_train,X_train)\n",
    "model = mlr.fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will create our model with **sklearn**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "mlr = LinearRegression()\n",
    "model = mlr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept of Model-Bias:  2.9490110405821124\n",
      "Coefficients of Model:  [ 0.0472521   0.179922   -0.00094408]\n"
     ]
    }
   ],
   "source": [
    "print(\"Intercept of Model-Bias: \",model.intercept_)\n",
    "print(\"Coefficients of Model: \",model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sales: 2.9490110405821124  +  0.047252095373716226 * TV + 0.17992200017716242 * Radio + -0.0009440773964715451 * Newspaper\n"
     ]
    }
   ],
   "source": [
    "print(\"Sales:\", model.intercept_ ,\" + \",\n",
    "      model.coef_[0],\"* TV +\",\n",
    "      model.coef_[1],\"* Radio +\",\n",
    "      model.coef_[2],\"* Newspaper\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's predict a spesific value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.15879089936421"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(pd.DataFrame([[35],[20],[45]]).T).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "train_root_mean_square = np.sqrt(mean_squared_error(y_train,model.predict(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root_mean_square_error of Training Set:  1.6798191851735351\n"
     ]
    }
   ],
   "source": [
    "print(\"root_mean_square_error of Training Set: \",train_root_mean_square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "test_root_mean_square = np.sqrt(mean_squared_error(y_test,model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root_mean_square_error of Test Set:  1.6706720762245335\n"
     ]
    }
   ],
   "source": [
    "print(\"root_mean_square_error of Test Set: \",test_root_mean_square)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X = ads.drop(\"sales\",axis=1)\n",
    "y = ads[\"sales\"]\n",
    "\n",
    "X_train = X.iloc[:160]\n",
    "X_test = X.iloc[160:]\n",
    "y_train = y[:160]\n",
    "y_test = y[160:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "mlr = LinearRegression()\n",
    "model = mlr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6798191851735351"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_train,model.predict(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8961523241120161"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's calculate validated r2 score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8853562237979616"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(model,X,y,cv=10,scoring=\"r2\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8712887522461431"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Rsquare\n",
    "cross_val_score(model,X_train,y_train,cv=20,scoring=\"r2\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.39622786, 1.30470686, 1.25871368, 1.77789447, 2.16086571,\n",
       "       1.00994124, 1.6878759 , 1.65332985, 1.19144572, 1.99369545,\n",
       "       1.18494422, 1.32926363, 1.66253245, 1.23768873, 1.03785675,\n",
       "       1.85464121, 3.787064  , 1.1191104 , 1.7032383 , 1.31822474])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(-cross_val_score(model,X_train,y_train,cv=20,scoring=\"neg_mean_squared_error\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6334630583441867"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(-cross_val_score(model,X_train,y_train,cv=20,scoring=\"neg_mean_squared_error\")).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.040749652544929"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Rsquare\n",
    "cross_val_score(model,X_test,y_test,cv=20,scoring=\"r2\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.33967605, 0.29261514, 2.32724211, 3.69122717, 1.25153914,\n",
       "       0.44182402, 0.76638645, 1.80852553, 0.43124828, 2.23780042,\n",
       "       0.78410944, 2.01829226, 1.08893429, 0.79032986, 1.14302882,\n",
       "       2.68133718, 0.85296659, 1.62944323, 1.49108901, 1.31986338])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(-cross_val_score(model,X_test,y_test,cv=20,scoring=\"neg_mean_squared_error\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4193739195301398"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(-cross_val_score(model,X_test,y_test,cv=20,scoring=\"neg_mean_squared_error\")).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Least-Squares Regression(Ordinary Least Squares)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The most common method for fitting a regression line is the method of least-squares. This method calculates the best-fitting line for the observed data by minimizing the sum of the squares of the vertical deviations from each data point to the line (if a point lies on the fitted line exactly, then its vertical deviation is 0). Because the deviations are first squared, then summed, there are no cancellations between positive and negative values.\n",
    "\n",
    "The Ordinary Least Squares procedure seeks to minimize the sum of the squared residuals. This means that given a regression line through the data we calculate the distance from each data point to the regression line, square it, and sum all of the squared errors together. This is the quantity that ordinary least squares seeks to minimize."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This photo is taken by: https://miro.medium.com/max/2366/1*tQkyTR9yxDcS1GKVFhdQQA.jpeg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Principal Component Analysis (PCA) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Principal Component Analysis, or PCA for short, is a method for reducing the dimensionality of data.\n",
    "\n",
    "It can be thought of as a projection method where data with m-columns (features) is projected into a subspace with m or fewer columns, whilst retaining the essence of the original data.\n",
    "\n",
    "The PCA method can be described and implemented using the tools of linear algebra."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For a real world example, we will work with **Hitters** dataset.\n",
    "\n",
    "It can be downloaded here: https://www.kaggle.com/floser/hitters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>446</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n",
       "0    293    66      1    30   29     14      1     293     66       1     30   \n",
       "1    315    81      7    24   38     39     14    3449    835      69    321   \n",
       "2    479   130     18    66   72     76      3    1624    457      63    224   \n",
       "3    496   141     20    65   78     37     11    5628   1575     225    828   \n",
       "4    321    87     10    39   42     30      2     396    101      12     48   \n",
       "\n",
       "   CRBI  CWalks League Division  PutOuts  Assists  Errors  Salary NewLeague  \n",
       "0    29      14      A        E      446       33      20     NaN         A  \n",
       "1   414     375      N        W      632       43      10   475.0         N  \n",
       "2   266     263      A        W      880       82      14   480.0         A  \n",
       "3   838     354      N        E      200       11       3   500.0         N  \n",
       "4    46      33      N        E      805       40       4    91.5         N  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts = pd.read_csv(\"Hitters.csv\")\n",
    "hts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will remove NA values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "hts.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>403.642586</td>\n",
       "      <td>147.307209</td>\n",
       "      <td>19.0</td>\n",
       "      <td>282.5</td>\n",
       "      <td>413.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>687.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>107.828897</td>\n",
       "      <td>45.125326</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>103.0</td>\n",
       "      <td>141.5</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>11.619772</td>\n",
       "      <td>8.757108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>263.0</td>\n",
       "      <td>54.745247</td>\n",
       "      <td>25.539816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>51.486692</td>\n",
       "      <td>25.882714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>41.114068</td>\n",
       "      <td>21.718056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years</th>\n",
       "      <td>263.0</td>\n",
       "      <td>7.311787</td>\n",
       "      <td>4.793616</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>2657.543726</td>\n",
       "      <td>2286.582929</td>\n",
       "      <td>19.0</td>\n",
       "      <td>842.5</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>3890.5</td>\n",
       "      <td>14053.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>722.186312</td>\n",
       "      <td>648.199644</td>\n",
       "      <td>4.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>4256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>69.239544</td>\n",
       "      <td>82.197581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>92.5</td>\n",
       "      <td>548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRuns</th>\n",
       "      <td>263.0</td>\n",
       "      <td>361.220532</td>\n",
       "      <td>331.198571</td>\n",
       "      <td>2.0</td>\n",
       "      <td>105.5</td>\n",
       "      <td>250.0</td>\n",
       "      <td>497.5</td>\n",
       "      <td>2165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>330.418251</td>\n",
       "      <td>323.367668</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>424.5</td>\n",
       "      <td>1659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CWalks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>260.266160</td>\n",
       "      <td>264.055868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>328.5</td>\n",
       "      <td>1566.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PutOuts</th>\n",
       "      <td>263.0</td>\n",
       "      <td>290.711027</td>\n",
       "      <td>279.934575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.5</td>\n",
       "      <td>224.0</td>\n",
       "      <td>322.5</td>\n",
       "      <td>1377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assists</th>\n",
       "      <td>263.0</td>\n",
       "      <td>118.760456</td>\n",
       "      <td>145.080577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Errors</th>\n",
       "      <td>263.0</td>\n",
       "      <td>8.593156</td>\n",
       "      <td>6.606574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>263.0</td>\n",
       "      <td>535.925882</td>\n",
       "      <td>451.118681</td>\n",
       "      <td>67.5</td>\n",
       "      <td>190.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2460.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std   min    25%     50%     75%      max\n",
       "AtBat    263.0   403.642586   147.307209  19.0  282.5   413.0   526.0    687.0\n",
       "Hits     263.0   107.828897    45.125326   1.0   71.5   103.0   141.5    238.0\n",
       "HmRun    263.0    11.619772     8.757108   0.0    5.0     9.0    18.0     40.0\n",
       "Runs     263.0    54.745247    25.539816   0.0   33.5    52.0    73.0    130.0\n",
       "RBI      263.0    51.486692    25.882714   0.0   30.0    47.0    71.0    121.0\n",
       "Walks    263.0    41.114068    21.718056   0.0   23.0    37.0    57.0    105.0\n",
       "Years    263.0     7.311787     4.793616   1.0    4.0     6.0    10.0     24.0\n",
       "CAtBat   263.0  2657.543726  2286.582929  19.0  842.5  1931.0  3890.5  14053.0\n",
       "CHits    263.0   722.186312   648.199644   4.0  212.0   516.0  1054.0   4256.0\n",
       "CHmRun   263.0    69.239544    82.197581   0.0   15.0    40.0    92.5    548.0\n",
       "CRuns    263.0   361.220532   331.198571   2.0  105.5   250.0   497.5   2165.0\n",
       "CRBI     263.0   330.418251   323.367668   3.0   95.0   230.0   424.5   1659.0\n",
       "CWalks   263.0   260.266160   264.055868   1.0   71.0   174.0   328.5   1566.0\n",
       "PutOuts  263.0   290.711027   279.934575   0.0  113.5   224.0   322.5   1377.0\n",
       "Assists  263.0   118.760456   145.080577   0.0    8.0    45.0   192.0    492.0\n",
       "Errors   263.0     8.593156     6.606574   0.0    3.0     7.0    13.0     32.0\n",
       "Salary   263.0   535.925882   451.118681  67.5  190.0   425.0   750.0   2460.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We will do **One Hot Encoding** to categorical columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>League_A</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_E</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_A</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   League_A  League_N  Division_E  Division_W  NewLeague_A  NewLeague_N\n",
       "1         0         1           0           1            0            1\n",
       "2         1         0           0           1            1            0\n",
       "3         0         1           1           0            0            1\n",
       "4         0         1           1           0            0            1\n",
       "5         1         0           0           1            1            0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoded = pd.get_dummies(hts[[\"League\",\"Division\",\"NewLeague\"]])\n",
    "one_hot_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "new_hts = hts.drop([\"League\",\"Division\",\"NewLeague\",\"Salary\"],axis=1).astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat   Hits  HmRun  Runs   RBI  Walks  Years  CAtBat   CHits  CHmRun  \\\n",
       "1  315.0   81.0    7.0  24.0  38.0   39.0   14.0  3449.0   835.0    69.0   \n",
       "2  479.0  130.0   18.0  66.0  72.0   76.0    3.0  1624.0   457.0    63.0   \n",
       "3  496.0  141.0   20.0  65.0  78.0   37.0   11.0  5628.0  1575.0   225.0   \n",
       "4  321.0   87.0   10.0  39.0  42.0   30.0    2.0   396.0   101.0    12.0   \n",
       "5  594.0  169.0    4.0  74.0  51.0   35.0   11.0  4408.0  1133.0    19.0   \n",
       "\n",
       "   CRuns   CRBI  CWalks  PutOuts  Assists  Errors  \n",
       "1  321.0  414.0   375.0    632.0     43.0    10.0  \n",
       "2  224.0  266.0   263.0    880.0     82.0    14.0  \n",
       "3  828.0  838.0   354.0    200.0     11.0     3.0  \n",
       "4   48.0   46.0    33.0    805.0     40.0     4.0  \n",
       "5  501.0  336.0   194.0    282.0    421.0    25.0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_hts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat   Hits  HmRun  Runs   RBI  Walks  Years  CAtBat   CHits  CHmRun  \\\n",
       "1  315.0   81.0    7.0  24.0  38.0   39.0   14.0  3449.0   835.0    69.0   \n",
       "2  479.0  130.0   18.0  66.0  72.0   76.0    3.0  1624.0   457.0    63.0   \n",
       "3  496.0  141.0   20.0  65.0  78.0   37.0   11.0  5628.0  1575.0   225.0   \n",
       "4  321.0   87.0   10.0  39.0  42.0   30.0    2.0   396.0   101.0    12.0   \n",
       "5  594.0  169.0    4.0  74.0  51.0   35.0   11.0  4408.0  1133.0    19.0   \n",
       "\n",
       "   CRuns   CRBI  CWalks  PutOuts  Assists  Errors  League_N  Division_W  \\\n",
       "1  321.0  414.0   375.0    632.0     43.0    10.0         1           1   \n",
       "2  224.0  266.0   263.0    880.0     82.0    14.0         0           1   \n",
       "3  828.0  838.0   354.0    200.0     11.0     3.0         1           0   \n",
       "4   48.0   46.0    33.0    805.0     40.0     4.0         1           0   \n",
       "5  501.0  336.0   194.0    282.0    421.0    25.0         0           1   \n",
       "\n",
       "   NewLeague_N  \n",
       "1            1  \n",
       "2            0  \n",
       "3            1  \n",
       "4            1  \n",
       "5            0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.concat([new_hts,one_hot_encoded[[\"League_N\",\"Division_W\",\"NewLeague_N\"]]],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y = hts.Salary # Target-dependent variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will split our dataset as train and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 19)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263,)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape:  (200, 19)\n",
      "X_test Shape:  (63, 19)\n",
      "y_train Shape:  (200,)\n",
      "y_test Shape:  (63,)\n"
     ]
    }
   ],
   "source": [
    "X_train = X.iloc[:200]\n",
    "X_test = X.iloc[200:]\n",
    "y_train = y[:200]\n",
    "y_test = y[200:]\n",
    "\n",
    "print(\"X_train Shape: \",X_train.shape)\n",
    "print(\"X_test Shape: \",X_test.shape)\n",
    "print(\"y_train Shape: \",y_train.shape)\n",
    "print(\"y_test Shape: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's do dimensionality reduction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca = PCA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_reduced_train = pca.fit_transform(scale(X_train))\n",
    "X_reduced_test = pca.fit_transform(scale(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.20734617,  1.90225323,  1.18586022, -1.05691602, -0.73617579,\n",
       "         1.39233919, -0.49250059, -0.03502036, -0.38427368, -0.5451096 ,\n",
       "         0.28689857, -0.80502297, -0.3405726 ,  0.27142567, -0.11867173,\n",
       "         0.12372502,  0.05853784, -0.03382087, -0.01148623],\n",
       "       [ 0.42919978, -2.53723573, -0.31064271, -0.10692387, -1.13001336,\n",
       "         2.29286939, -0.18778909,  1.08504905, -0.49014462,  0.08165734,\n",
       "         0.13830876, -0.04016291,  0.38495629, -0.02677618, -0.02755964,\n",
       "         0.14160341,  0.00624337, -0.03205239, -0.00695663],\n",
       "       [ 3.28644219,  0.90175579,  0.19359382, -1.53919197,  0.92146129,\n",
       "        -1.26707784, -0.69842099, -0.87711054,  0.1956631 ,  0.81098551,\n",
       "        -0.01807494, -0.04005277, -0.0520347 , -0.43859559, -0.2246159 ,\n",
       "        -0.04678025, -0.17619738, -0.03800044, -0.06429503],\n",
       "       [-2.57117645, -0.36054003,  0.25655259, -2.11139788,  1.22913201,\n",
       "         1.58852743, -0.62162344, -0.31816179,  0.32886983,  0.11813513,\n",
       "        -0.06594783,  0.13476699,  0.11910362, -0.01628155, -0.03901438,\n",
       "         0.01309992, -0.05136924, -0.00704776, -0.01717479],\n",
       "       [ 1.02707153, -1.41065385,  2.24505028,  3.51378833, -0.63015452,\n",
       "         0.70751124,  0.41471087, -0.99873453, -0.65120351, -0.20008127,\n",
       "        -0.02758645, -0.21608234, -0.30590223, -0.07515173, -0.06682995,\n",
       "         0.02206276, -0.0514767 , -0.12286677, -0.06635007]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_reduced_train[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's see cumulative explanatioon rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36. , 60.6, 72.6, 80.4, 85.3, 89.3])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(np.round(pca.explained_variance_ratio_,decimals=3)*100)[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Principal Component Regression(PCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). More specifically, PCR is used for estimating the unknown regression coefficients in a standard linear regression model.\n",
    "\n",
    "We first perform principal components analysis (PCA) on the original data, then perform dimension reduction by selecting the number of principal components (m) using cross-validation or test set error, and finally conduct regression using the first m dimension reduced principal components.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For a real world example, we will work with **Hitters** dataset.\n",
    "\n",
    "It can be downloaded here: https://www.kaggle.com/floser/hitters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>293</td>\n",
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       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>446</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n",
       "0    293    66      1    30   29     14      1     293     66       1     30   \n",
       "1    315    81      7    24   38     39     14    3449    835      69    321   \n",
       "2    479   130     18    66   72     76      3    1624    457      63    224   \n",
       "3    496   141     20    65   78     37     11    5628   1575     225    828   \n",
       "4    321    87     10    39   42     30      2     396    101      12     48   \n",
       "\n",
       "   CRBI  CWalks League Division  PutOuts  Assists  Errors  Salary NewLeague  \n",
       "0    29      14      A        E      446       33      20     NaN         A  \n",
       "1   414     375      N        W      632       43      10   475.0         N  \n",
       "2   266     263      A        W      880       82      14   480.0         A  \n",
       "3   838     354      N        E      200       11       3   500.0         N  \n",
       "4    46      33      N        E      805       40       4    91.5         N  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts = pd.read_csv(\"Hitters.csv\")\n",
    "hts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will remove NA values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "hts.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>403.642586</td>\n",
       "      <td>147.307209</td>\n",
       "      <td>19.0</td>\n",
       "      <td>282.5</td>\n",
       "      <td>413.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>687.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>107.828897</td>\n",
       "      <td>45.125326</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>103.0</td>\n",
       "      <td>141.5</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>11.619772</td>\n",
       "      <td>8.757108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>263.0</td>\n",
       "      <td>54.745247</td>\n",
       "      <td>25.539816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>51.486692</td>\n",
       "      <td>25.882714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>41.114068</td>\n",
       "      <td>21.718056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years</th>\n",
       "      <td>263.0</td>\n",
       "      <td>7.311787</td>\n",
       "      <td>4.793616</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>2657.543726</td>\n",
       "      <td>2286.582929</td>\n",
       "      <td>19.0</td>\n",
       "      <td>842.5</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>3890.5</td>\n",
       "      <td>14053.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>722.186312</td>\n",
       "      <td>648.199644</td>\n",
       "      <td>4.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>4256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>69.239544</td>\n",
       "      <td>82.197581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>92.5</td>\n",
       "      <td>548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRuns</th>\n",
       "      <td>263.0</td>\n",
       "      <td>361.220532</td>\n",
       "      <td>331.198571</td>\n",
       "      <td>2.0</td>\n",
       "      <td>105.5</td>\n",
       "      <td>250.0</td>\n",
       "      <td>497.5</td>\n",
       "      <td>2165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>330.418251</td>\n",
       "      <td>323.367668</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>424.5</td>\n",
       "      <td>1659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CWalks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>260.266160</td>\n",
       "      <td>264.055868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>328.5</td>\n",
       "      <td>1566.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PutOuts</th>\n",
       "      <td>263.0</td>\n",
       "      <td>290.711027</td>\n",
       "      <td>279.934575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.5</td>\n",
       "      <td>224.0</td>\n",
       "      <td>322.5</td>\n",
       "      <td>1377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assists</th>\n",
       "      <td>263.0</td>\n",
       "      <td>118.760456</td>\n",
       "      <td>145.080577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Errors</th>\n",
       "      <td>263.0</td>\n",
       "      <td>8.593156</td>\n",
       "      <td>6.606574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>263.0</td>\n",
       "      <td>535.925882</td>\n",
       "      <td>451.118681</td>\n",
       "      <td>67.5</td>\n",
       "      <td>190.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2460.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std   min    25%     50%     75%      max\n",
       "AtBat    263.0   403.642586   147.307209  19.0  282.5   413.0   526.0    687.0\n",
       "Hits     263.0   107.828897    45.125326   1.0   71.5   103.0   141.5    238.0\n",
       "HmRun    263.0    11.619772     8.757108   0.0    5.0     9.0    18.0     40.0\n",
       "Runs     263.0    54.745247    25.539816   0.0   33.5    52.0    73.0    130.0\n",
       "RBI      263.0    51.486692    25.882714   0.0   30.0    47.0    71.0    121.0\n",
       "Walks    263.0    41.114068    21.718056   0.0   23.0    37.0    57.0    105.0\n",
       "Years    263.0     7.311787     4.793616   1.0    4.0     6.0    10.0     24.0\n",
       "CAtBat   263.0  2657.543726  2286.582929  19.0  842.5  1931.0  3890.5  14053.0\n",
       "CHits    263.0   722.186312   648.199644   4.0  212.0   516.0  1054.0   4256.0\n",
       "CHmRun   263.0    69.239544    82.197581   0.0   15.0    40.0    92.5    548.0\n",
       "CRuns    263.0   361.220532   331.198571   2.0  105.5   250.0   497.5   2165.0\n",
       "CRBI     263.0   330.418251   323.367668   3.0   95.0   230.0   424.5   1659.0\n",
       "CWalks   263.0   260.266160   264.055868   1.0   71.0   174.0   328.5   1566.0\n",
       "PutOuts  263.0   290.711027   279.934575   0.0  113.5   224.0   322.5   1377.0\n",
       "Assists  263.0   118.760456   145.080577   0.0    8.0    45.0   192.0    492.0\n",
       "Errors   263.0     8.593156     6.606574   0.0    3.0     7.0    13.0     32.0\n",
       "Salary   263.0   535.925882   451.118681  67.5  190.0   425.0   750.0   2460.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We will do **One Hot Encoding** to categorical columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>League_A</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_E</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_A</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   League_A  League_N  Division_E  Division_W  NewLeague_A  NewLeague_N\n",
       "1         0         1           0           1            0            1\n",
       "2         1         0           0           1            1            0\n",
       "3         0         1           1           0            0            1\n",
       "4         0         1           1           0            0            1\n",
       "5         1         0           0           1            1            0"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoded = pd.get_dummies(hts[[\"League\",\"Division\",\"NewLeague\"]])\n",
    "one_hot_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "new_hts = hts.drop([\"League\",\"Division\",\"NewLeague\",\"Salary\"],axis=1).astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
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       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat   Hits  HmRun  Runs   RBI  Walks  Years  CAtBat   CHits  CHmRun  \\\n",
       "1  315.0   81.0    7.0  24.0  38.0   39.0   14.0  3449.0   835.0    69.0   \n",
       "2  479.0  130.0   18.0  66.0  72.0   76.0    3.0  1624.0   457.0    63.0   \n",
       "3  496.0  141.0   20.0  65.0  78.0   37.0   11.0  5628.0  1575.0   225.0   \n",
       "4  321.0   87.0   10.0  39.0  42.0   30.0    2.0   396.0   101.0    12.0   \n",
       "5  594.0  169.0    4.0  74.0  51.0   35.0   11.0  4408.0  1133.0    19.0   \n",
       "\n",
       "   CRuns   CRBI  CWalks  PutOuts  Assists  Errors  League_N  Division_W  \\\n",
       "1  321.0  414.0   375.0    632.0     43.0    10.0         1           1   \n",
       "2  224.0  266.0   263.0    880.0     82.0    14.0         0           1   \n",
       "3  828.0  838.0   354.0    200.0     11.0     3.0         1           0   \n",
       "4   48.0   46.0    33.0    805.0     40.0     4.0         1           0   \n",
       "5  501.0  336.0   194.0    282.0    421.0    25.0         0           1   \n",
       "\n",
       "   NewLeague_N  \n",
       "1            1  \n",
       "2            0  \n",
       "3            1  \n",
       "4            1  \n",
       "5            0  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.concat([new_hts,one_hot_encoded[[\"League_N\",\"Division_W\",\"NewLeague_N\"]]],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y = hts.Salary # Target-dependent variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will split our dataset as train and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 20)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 19)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Independent Variables\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263,)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dependent Variables\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape:  (210, 19)\n",
      "X_test Shape:  (53, 19)\n",
      "y_train Shape:  (210,)\n",
      "y_test Shape:  (53,)\n"
     ]
    }
   ],
   "source": [
    "X_train = X.iloc[:210]\n",
    "X_test = X.iloc[210:]\n",
    "y_train = y[:210]\n",
    "y_test = y[210:]\n",
    "\n",
    "print(\"X_train Shape: \",X_train.shape)\n",
    "print(\"X_test Shape: \",X_test.shape)\n",
    "print(\"y_train Shape: \",y_train.shape)\n",
    "print(\"y_test Shape: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's do dimensionality reduction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca = PCA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_reduced_train = pca.fit_transform(scale(X_train))\n",
    "X_reduced_test = pca.fit_transform(scale(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's see cumulative explanatioon rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([34.5, 60. , 72.6, 81.5, 86.5, 90.4])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(np.round(pca.explained_variance_ratio_,decimals=3)*100)[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's create a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pcr = LinearRegression()\n",
    "pcr_model = pcr.fit(X_reduced_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept:  546.6944714285714\n",
      "Coefficients:  [ 108.62906365  -24.35077093   23.95595793  -34.77184272  -83.14014623\n",
      "   71.39286412    8.80356221   -9.69607076   54.01473132  136.14645851\n",
      "    1.49578456   13.67658164   38.45645007 -264.2061819   170.91181041\n",
      "  453.57803418 -410.34451125  673.85990715 -419.38631999]\n"
     ]
    }
   ],
   "source": [
    "print(\"Intercept: \",pcr_model.intercept_)\n",
    "print(\"Coefficients: \",pcr_model.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 403.21993393,  758.41542239, 1182.40881941,  650.66137637,\n",
       "        578.96379589,  190.88965559,  106.06128243,  193.0449271 ,\n",
       "        657.00809147,  968.70200341])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = pcr_model.predict(X_reduced_train)\n",
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303.22898816258595"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#root mean square error for Train Set\n",
    "np.sqrt(mean_squared_error(y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5752262816943401"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#r2 score for Train Set\n",
    "r2_score(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 504.53657011,  569.42395377,  194.53967032,  240.45906447,\n",
       "        503.91439963, 1141.51766146,   33.93846198,  378.63296484,\n",
       "       1320.04133275,  806.28367883])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = pcr_model.predict(X_reduced_test)\n",
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "335.632324774888"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#root mean square error for Test Set\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22873295246020364"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#r2 score for Test Set\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean square error:  335.632324774888\n"
     ]
    }
   ],
   "source": [
    "pcr = LinearRegression()\n",
    "pcr_model = pcr.fit(X_reduced_train,y_train)\n",
    "y_pred = pcr_model.predict(X_reduced_test)\n",
    "print(\"Root mean square error: \",np.sqrt(mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's try different dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean square error:  302.6167604337664\n"
     ]
    }
   ],
   "source": [
    "pcr_model = pcr.fit(X_reduced_train[:,:3],y_train)\n",
    "y_pred = pcr_model.predict(X_reduced_test[:,:3])\n",
    "print(\"Root mean square error: \",np.sqrt(mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cross_val = model_selection.KFold(n_splits=7,\n",
    "                                  shuffle=True,\n",
    "                                  random_state=45)\n",
    "pcr = LinearRegression()\n",
    "Root_mean_sqaure_error = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210, 19)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_reduced_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_base.py\", line 505, in fit\n",
      "    X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py\", line 432, in _validate_data\n",
      "    X, y = check_X_y(X, y, **check_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 795, in check_X_y\n",
      "    X = check_array(X, accept_sparse=accept_sparse,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 658, in check_array\n",
      "    raise ValueError(\"Found array with %d feature(s) (shape=%s) while\"\n",
      "ValueError: Found array with 0 feature(s) (shape=(180, 0)) while a minimum of 1 is required.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_base.py\", line 505, in fit\n",
      "    X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py\", line 432, in _validate_data\n",
      "    X, y = check_X_y(X, y, **check_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 795, in check_X_y\n",
      "    X = check_array(X, accept_sparse=accept_sparse,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 658, in check_array\n",
      "    raise ValueError(\"Found array with %d feature(s) (shape=%s) while\"\n",
      "ValueError: Found array with 0 feature(s) (shape=(180, 0)) while a minimum of 1 is required.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_base.py\", line 505, in fit\n",
      "    X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py\", line 432, in _validate_data\n",
      "    X, y = check_X_y(X, y, **check_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 795, in check_X_y\n",
      "    X = check_array(X, accept_sparse=accept_sparse,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 658, in check_array\n",
      "    raise ValueError(\"Found array with %d feature(s) (shape=%s) while\"\n",
      "ValueError: Found array with 0 feature(s) (shape=(180, 0)) while a minimum of 1 is required.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_base.py\", line 505, in fit\n",
      "    X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py\", line 432, in _validate_data\n",
      "    X, y = check_X_y(X, y, **check_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 795, in check_X_y\n",
      "    X = check_array(X, accept_sparse=accept_sparse,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 658, in check_array\n",
      "    raise ValueError(\"Found array with %d feature(s) (shape=%s) while\"\n",
      "ValueError: Found array with 0 feature(s) (shape=(180, 0)) while a minimum of 1 is required.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_base.py\", line 505, in fit\n",
      "    X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py\", line 432, in _validate_data\n",
      "    X, y = check_X_y(X, y, **check_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 795, in check_X_y\n",
      "    X = check_array(X, accept_sparse=accept_sparse,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 658, in check_array\n",
      "    raise ValueError(\"Found array with %d feature(s) (shape=%s) while\"\n",
      "ValueError: Found array with 0 feature(s) (shape=(180, 0)) while a minimum of 1 is required.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_base.py\", line 505, in fit\n",
      "    X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py\", line 432, in _validate_data\n",
      "    X, y = check_X_y(X, y, **check_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 795, in check_X_y\n",
      "    X = check_array(X, accept_sparse=accept_sparse,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 658, in check_array\n",
      "    raise ValueError(\"Found array with %d feature(s) (shape=%s) while\"\n",
      "ValueError: Found array with 0 feature(s) (shape=(180, 0)) while a minimum of 1 is required.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_base.py\", line 505, in fit\n",
      "    X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py\", line 432, in _validate_data\n",
      "    X, y = check_X_y(X, y, **check_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 795, in check_X_y\n",
      "    X = check_array(X, accept_sparse=accept_sparse,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 72, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 658, in check_array\n",
      "    raise ValueError(\"Found array with %d feature(s) (shape=%s) while\"\n",
      "ValueError: Found array with 0 feature(s) (shape=(180, 0)) while a minimum of 1 is required.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    }
   ],
   "source": [
    "for num in np.arange(X_reduced_train.shape[1]+1):\n",
    "    score = np.sqrt(-1*model_selection.cross_val_score(pcr,X_reduced_train[:,:num],y_train.ravel(),\n",
    "                                                       cv=cross_val,scoring=\"neg_mean_squared_error\")).mean()\n",
    "    \n",
    "    Root_mean_sqaure_error.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Root_mean_sqaure_error,\"-v\",c=\"r\")\n",
    "plt.xlabel(\"Component Numbers\")\n",
    "plt.ylabel(\"Root_mean_sqaure_error\")\n",
    "plt.title(\"PCR Model Tuning\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Optimum value seems 6. Now we will create a model with 6 components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pcr = LinearRegression()\n",
    "pcr_model = pcr.fit(X_reduced_train[:,:6],y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean square error for Train set:  333.4936273726116\n"
     ]
    }
   ],
   "source": [
    "y_pred = pcr_model.predict(X_reduced_train[:,:6])\n",
    "print(\"Root mean square error for Train set: \",np.sqrt(mean_squared_error(y_train,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean square error for Text set:  289.76613807162045\n"
     ]
    }
   ],
   "source": [
    "y_pred = pcr_model.predict(X_reduced_test[:,:6])\n",
    "print(\"Root mean square error for Text set: \",np.sqrt(mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Shrinkage(Regularization) Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "A good way to reduce overfitting is to regularize the model (i.e., to constrain it): the fewer degrees of freedom it has, the harder it will be for it to overfit the data. A simple way to regularize a polynomial model is to reduce the number of polynomial degrees.\n",
    "\n",
    "For a linear model, regularization is typically achieved by constraining the weights of the model. We will now look at Partial Least Squares, Ridge Regression, Lasso Regression, and Elastic Net, which implement four different ways to constrain the weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Partial Least Squares (PLS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Partial Least Squares regression (PLS) is a quick, efficient and optimal regression method based on covariance. It is recommended in cases of regression where the number of explanatory variables is high, and where it is likely that the explanatory variables are correlated.\n",
    "\n",
    "PLS helps to handle the case of a large number of correlated independent variables, which is common in chemometrics. Itis an alternative to ordinary least squares (OLS) regression. Firstly it extracts a set of latent factors that explain as much of the covariance as possible between the independent and dependent variables. Then a regression step predicts values of the dependent variables using the decomposition of the independent variables.\n",
    "\n",
    "PLS is a supevised dimensiion reduction method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For a real world example, we will work with **Hitters** dataset.\n",
    "\n",
    "It can be downloaded here: https://www.kaggle.com/floser/hitters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>446</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n",
       "0    293    66      1    30   29     14      1     293     66       1     30   \n",
       "1    315    81      7    24   38     39     14    3449    835      69    321   \n",
       "2    479   130     18    66   72     76      3    1624    457      63    224   \n",
       "3    496   141     20    65   78     37     11    5628   1575     225    828   \n",
       "4    321    87     10    39   42     30      2     396    101      12     48   \n",
       "\n",
       "   CRBI  CWalks League Division  PutOuts  Assists  Errors  Salary NewLeague  \n",
       "0    29      14      A        E      446       33      20     NaN         A  \n",
       "1   414     375      N        W      632       43      10   475.0         N  \n",
       "2   266     263      A        W      880       82      14   480.0         A  \n",
       "3   838     354      N        E      200       11       3   500.0         N  \n",
       "4    46      33      N        E      805       40       4    91.5         N  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts = pd.read_csv(\"Hitters.csv\")\n",
    "hts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will remove NA values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "hts.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>403.642586</td>\n",
       "      <td>147.307209</td>\n",
       "      <td>19.0</td>\n",
       "      <td>282.5</td>\n",
       "      <td>413.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>687.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>107.828897</td>\n",
       "      <td>45.125326</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>103.0</td>\n",
       "      <td>141.5</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>11.619772</td>\n",
       "      <td>8.757108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>263.0</td>\n",
       "      <td>54.745247</td>\n",
       "      <td>25.539816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>51.486692</td>\n",
       "      <td>25.882714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>41.114068</td>\n",
       "      <td>21.718056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years</th>\n",
       "      <td>263.0</td>\n",
       "      <td>7.311787</td>\n",
       "      <td>4.793616</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>2657.543726</td>\n",
       "      <td>2286.582929</td>\n",
       "      <td>19.0</td>\n",
       "      <td>842.5</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>3890.5</td>\n",
       "      <td>14053.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>722.186312</td>\n",
       "      <td>648.199644</td>\n",
       "      <td>4.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>4256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>69.239544</td>\n",
       "      <td>82.197581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>92.5</td>\n",
       "      <td>548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRuns</th>\n",
       "      <td>263.0</td>\n",
       "      <td>361.220532</td>\n",
       "      <td>331.198571</td>\n",
       "      <td>2.0</td>\n",
       "      <td>105.5</td>\n",
       "      <td>250.0</td>\n",
       "      <td>497.5</td>\n",
       "      <td>2165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>330.418251</td>\n",
       "      <td>323.367668</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>424.5</td>\n",
       "      <td>1659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CWalks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>260.266160</td>\n",
       "      <td>264.055868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>328.5</td>\n",
       "      <td>1566.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PutOuts</th>\n",
       "      <td>263.0</td>\n",
       "      <td>290.711027</td>\n",
       "      <td>279.934575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.5</td>\n",
       "      <td>224.0</td>\n",
       "      <td>322.5</td>\n",
       "      <td>1377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assists</th>\n",
       "      <td>263.0</td>\n",
       "      <td>118.760456</td>\n",
       "      <td>145.080577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Errors</th>\n",
       "      <td>263.0</td>\n",
       "      <td>8.593156</td>\n",
       "      <td>6.606574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>263.0</td>\n",
       "      <td>535.925882</td>\n",
       "      <td>451.118681</td>\n",
       "      <td>67.5</td>\n",
       "      <td>190.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2460.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std   min    25%     50%     75%      max\n",
       "AtBat    263.0   403.642586   147.307209  19.0  282.5   413.0   526.0    687.0\n",
       "Hits     263.0   107.828897    45.125326   1.0   71.5   103.0   141.5    238.0\n",
       "HmRun    263.0    11.619772     8.757108   0.0    5.0     9.0    18.0     40.0\n",
       "Runs     263.0    54.745247    25.539816   0.0   33.5    52.0    73.0    130.0\n",
       "RBI      263.0    51.486692    25.882714   0.0   30.0    47.0    71.0    121.0\n",
       "Walks    263.0    41.114068    21.718056   0.0   23.0    37.0    57.0    105.0\n",
       "Years    263.0     7.311787     4.793616   1.0    4.0     6.0    10.0     24.0\n",
       "CAtBat   263.0  2657.543726  2286.582929  19.0  842.5  1931.0  3890.5  14053.0\n",
       "CHits    263.0   722.186312   648.199644   4.0  212.0   516.0  1054.0   4256.0\n",
       "CHmRun   263.0    69.239544    82.197581   0.0   15.0    40.0    92.5    548.0\n",
       "CRuns    263.0   361.220532   331.198571   2.0  105.5   250.0   497.5   2165.0\n",
       "CRBI     263.0   330.418251   323.367668   3.0   95.0   230.0   424.5   1659.0\n",
       "CWalks   263.0   260.266160   264.055868   1.0   71.0   174.0   328.5   1566.0\n",
       "PutOuts  263.0   290.711027   279.934575   0.0  113.5   224.0   322.5   1377.0\n",
       "Assists  263.0   118.760456   145.080577   0.0    8.0    45.0   192.0    492.0\n",
       "Errors   263.0     8.593156     6.606574   0.0    3.0     7.0    13.0     32.0\n",
       "Salary   263.0   535.925882   451.118681  67.5  190.0   425.0   750.0   2460.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We will do **One Hot Encoding** to categorical columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   League_A  League_N  Division_E  Division_W  NewLeague_A  NewLeague_N\n",
       "1         0         1           0           1            0            1\n",
       "2         1         0           0           1            1            0\n",
       "3         0         1           1           0            0            1\n",
       "4         0         1           1           0            0            1\n",
       "5         1         0           0           1            1            0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoded = pd.get_dummies(hts[[\"League\",\"Division\",\"NewLeague\"]])\n",
    "one_hot_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "new_hts = hts.drop([\"League\",\"Division\",\"NewLeague\",\"Salary\"],axis=1).astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat   Hits  HmRun  Runs   RBI  Walks  Years  CAtBat   CHits  CHmRun  \\\n",
       "1  315.0   81.0    7.0  24.0  38.0   39.0   14.0  3449.0   835.0    69.0   \n",
       "2  479.0  130.0   18.0  66.0  72.0   76.0    3.0  1624.0   457.0    63.0   \n",
       "3  496.0  141.0   20.0  65.0  78.0   37.0   11.0  5628.0  1575.0   225.0   \n",
       "4  321.0   87.0   10.0  39.0  42.0   30.0    2.0   396.0   101.0    12.0   \n",
       "5  594.0  169.0    4.0  74.0  51.0   35.0   11.0  4408.0  1133.0    19.0   \n",
       "\n",
       "   CRuns   CRBI  CWalks  PutOuts  Assists  Errors  League_N  Division_W  \\\n",
       "1  321.0  414.0   375.0    632.0     43.0    10.0         1           1   \n",
       "2  224.0  266.0   263.0    880.0     82.0    14.0         0           1   \n",
       "3  828.0  838.0   354.0    200.0     11.0     3.0         1           0   \n",
       "4   48.0   46.0    33.0    805.0     40.0     4.0         1           0   \n",
       "5  501.0  336.0   194.0    282.0    421.0    25.0         0           1   \n",
       "\n",
       "   NewLeague_N  \n",
       "1            1  \n",
       "2            0  \n",
       "3            1  \n",
       "4            1  \n",
       "5            0  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.concat([new_hts,one_hot_encoded[[\"League_N\",\"Division_W\",\"NewLeague_N\"]]],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y = hts.Salary # Target-dependent variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will split our dataset as train and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 20)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 19)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Independent Variables\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263,)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dependent Variables\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape:  (210, 19)\n",
      "X_test Shape:  (53, 19)\n",
      "y_train Shape:  (210,)\n",
      "y_test Shape:  (53,)\n"
     ]
    }
   ],
   "source": [
    "X_train = X.iloc[:210]\n",
    "X_test = X.iloc[210:]\n",
    "y_train = y[:210]\n",
    "y_test = y[210:]\n",
    "\n",
    "print(\"X_train Shape: \",X_train.shape)\n",
    "print(\"X_test Shape: \",X_test.shape)\n",
    "print(\"y_train Shape: \",y_train.shape)\n",
    "print(\"y_test Shape: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pls_model = PLSRegression(n_components=7).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-182.50759387],\n",
       "       [ 169.37021424],\n",
       "       [ -47.77506515],\n",
       "       [  63.08891812],\n",
       "       [  30.88901468],\n",
       "       [  70.86551369],\n",
       "       [ -97.2013175 ],\n",
       "       [ -36.56263571],\n",
       "       [  93.75294012],\n",
       "       [ 126.62002648],\n",
       "       [ 122.21898969],\n",
       "       [ 155.47076042],\n",
       "       [-150.91672409],\n",
       "       [  84.28777431],\n",
       "       [  72.6812175 ],\n",
       "       [ -25.72515764],\n",
       "       [  17.93140121],\n",
       "       [ -92.7266382 ],\n",
       "       [  23.7217116 ]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pls_model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PLSRegression(n_components=7)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pls_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat   Hits  HmRun  Runs   RBI  Walks  Years  CAtBat   CHits  CHmRun  \\\n",
       "1  315.0   81.0    7.0  24.0  38.0   39.0   14.0  3449.0   835.0    69.0   \n",
       "2  479.0  130.0   18.0  66.0  72.0   76.0    3.0  1624.0   457.0    63.0   \n",
       "3  496.0  141.0   20.0  65.0  78.0   37.0   11.0  5628.0  1575.0   225.0   \n",
       "4  321.0   87.0   10.0  39.0  42.0   30.0    2.0   396.0   101.0    12.0   \n",
       "5  594.0  169.0    4.0  74.0  51.0   35.0   11.0  4408.0  1133.0    19.0   \n",
       "\n",
       "   CRuns   CRBI  CWalks  PutOuts  Assists  Errors  League_N  Division_W  \\\n",
       "1  321.0  414.0   375.0    632.0     43.0    10.0         1           1   \n",
       "2  224.0  266.0   263.0    880.0     82.0    14.0         0           1   \n",
       "3  828.0  838.0   354.0    200.0     11.0     3.0         1           0   \n",
       "4   48.0   46.0    33.0    805.0     40.0     4.0         1           0   \n",
       "5  501.0  336.0   194.0    282.0    421.0    25.0         0           1   \n",
       "\n",
       "   NewLeague_N  \n",
       "1            1  \n",
       "2            0  \n",
       "3            1  \n",
       "4            1  \n",
       "5            0  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 342.37839948],\n",
       "       [ 674.40578249],\n",
       "       [1215.11323091],\n",
       "       [ 633.48668554],\n",
       "       [ 551.9388145 ],\n",
       "       [ 256.98620543],\n",
       "       [ 109.55958772],\n",
       "       [ 174.82878563],\n",
       "       [ 761.60266771],\n",
       "       [ 965.67745006]])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pls_model.predict(X_train)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=pls_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "313.32632336572675"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Error\n",
    "np.sqrt(mean_squared_error(y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5464658750671466"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=pls_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "333.730196918961"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Error\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23745017859177564"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PLSRegression(n_components=7)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pls_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cross_val = model_selection.KFold(n_splits=15,\n",
    "                                  shuffle=True,\n",
    "                                  random_state=45)\n",
    "Root_mean_sqaure_error = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for num in np.arange(1,X_train.shape[1]+1):\n",
    "    pls= PLSRegression(n_components=num)\n",
    "    score = np.sqrt(-1*model_selection.cross_val_score(pls,X_train,y_train,\n",
    "                                                       cv=cross_val,scoring=\"neg_mean_squared_error\")).mean()\n",
    "    \n",
    "    Root_mean_sqaure_error.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Root_mean_sqaure_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(1,X_train.shape[1]+1),np.array(Root_mean_sqaure_error),\"-v\",c=\"g\")\n",
    "plt.xlabel(\"Component Numbers\")\n",
    "plt.ylabel(\"Root_mean_sqaure_error\")\n",
    "plt.title(\"PLS Model Tuning\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will create our last model with optimal value that seems 8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pls_model = PLSRegression(n_components=8).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=pls_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "309.05347867636664"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Error\n",
    "np.sqrt(mean_squared_error(y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5587512609460972"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=pls_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "330.6483176898801"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Error\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2514689001375435"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Ridge Regression ( L2 Regularization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Ridge Regression is a regularized version of Linear Regression. Ridge Regression, like Linear Regression, aims to minimize the Residual Sum of Squares(RSS) but with a slight change. While Linear Regression estimates the coefficients using the values that minimize the following equation:"
   ]
  },
  {
   "attachments": {
    "Screen%20Shot%202021-07-16%20at%2008.39.49.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![Screen%20Shot%202021-07-16%20at%2008.39.49.png](attachment:Screen%20Shot%202021-07-16%20at%2008.39.49.png)\n",
    "\n",
    "Photo is cited by here: http://busigence.com/blog/shrinkage-methods-in-linear-regression/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Ridge Regression adds a **penalty term(lambda 𝜆)** to this to shrink the coefficients to 0 :"
   ]
  },
  {
   "attachments": {
    "Screen%20Shot%202021-07-16%20at%2008.41.36.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![Screen%20Shot%202021-07-16%20at%2008.41.36.png](attachment:Screen%20Shot%202021-07-16%20at%2008.41.36.png)\n",
    "\n",
    "Photo is cited by here: http://busigence.com/blog/shrinkage-methods-in-linear-regression/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The hyperparameter *lambda(𝜆)* controls how much we want to regularize the model. If  𝜆 = 0, then Ridge Regression is just Linear Regression. If α is very large, then all weights end up very close to zero and the result is a flat line going through the data’s mean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For a real world example, we will work with **Hitters** dataset.\n",
    "\n",
    "It can be downloaded here: https://www.kaggle.com/floser/hitters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>446</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n",
       "0    293    66      1    30   29     14      1     293     66       1     30   \n",
       "1    315    81      7    24   38     39     14    3449    835      69    321   \n",
       "2    479   130     18    66   72     76      3    1624    457      63    224   \n",
       "3    496   141     20    65   78     37     11    5628   1575     225    828   \n",
       "4    321    87     10    39   42     30      2     396    101      12     48   \n",
       "\n",
       "   CRBI  CWalks League Division  PutOuts  Assists  Errors  Salary NewLeague  \n",
       "0    29      14      A        E      446       33      20     NaN         A  \n",
       "1   414     375      N        W      632       43      10   475.0         N  \n",
       "2   266     263      A        W      880       82      14   480.0         A  \n",
       "3   838     354      N        E      200       11       3   500.0         N  \n",
       "4    46      33      N        E      805       40       4    91.5         N  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts = pd.read_csv(\"Hitters.csv\")\n",
    "hts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will remove NA values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "hts.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>403.642586</td>\n",
       "      <td>147.307209</td>\n",
       "      <td>19.0</td>\n",
       "      <td>282.5</td>\n",
       "      <td>413.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>687.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>107.828897</td>\n",
       "      <td>45.125326</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>103.0</td>\n",
       "      <td>141.5</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>11.619772</td>\n",
       "      <td>8.757108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>263.0</td>\n",
       "      <td>54.745247</td>\n",
       "      <td>25.539816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>51.486692</td>\n",
       "      <td>25.882714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>41.114068</td>\n",
       "      <td>21.718056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years</th>\n",
       "      <td>263.0</td>\n",
       "      <td>7.311787</td>\n",
       "      <td>4.793616</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>2657.543726</td>\n",
       "      <td>2286.582929</td>\n",
       "      <td>19.0</td>\n",
       "      <td>842.5</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>3890.5</td>\n",
       "      <td>14053.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>722.186312</td>\n",
       "      <td>648.199644</td>\n",
       "      <td>4.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>4256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>69.239544</td>\n",
       "      <td>82.197581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>92.5</td>\n",
       "      <td>548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRuns</th>\n",
       "      <td>263.0</td>\n",
       "      <td>361.220532</td>\n",
       "      <td>331.198571</td>\n",
       "      <td>2.0</td>\n",
       "      <td>105.5</td>\n",
       "      <td>250.0</td>\n",
       "      <td>497.5</td>\n",
       "      <td>2165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>330.418251</td>\n",
       "      <td>323.367668</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>424.5</td>\n",
       "      <td>1659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CWalks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>260.266160</td>\n",
       "      <td>264.055868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>328.5</td>\n",
       "      <td>1566.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PutOuts</th>\n",
       "      <td>263.0</td>\n",
       "      <td>290.711027</td>\n",
       "      <td>279.934575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.5</td>\n",
       "      <td>224.0</td>\n",
       "      <td>322.5</td>\n",
       "      <td>1377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assists</th>\n",
       "      <td>263.0</td>\n",
       "      <td>118.760456</td>\n",
       "      <td>145.080577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Errors</th>\n",
       "      <td>263.0</td>\n",
       "      <td>8.593156</td>\n",
       "      <td>6.606574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>263.0</td>\n",
       "      <td>535.925882</td>\n",
       "      <td>451.118681</td>\n",
       "      <td>67.5</td>\n",
       "      <td>190.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2460.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std   min    25%     50%     75%      max\n",
       "AtBat    263.0   403.642586   147.307209  19.0  282.5   413.0   526.0    687.0\n",
       "Hits     263.0   107.828897    45.125326   1.0   71.5   103.0   141.5    238.0\n",
       "HmRun    263.0    11.619772     8.757108   0.0    5.0     9.0    18.0     40.0\n",
       "Runs     263.0    54.745247    25.539816   0.0   33.5    52.0    73.0    130.0\n",
       "RBI      263.0    51.486692    25.882714   0.0   30.0    47.0    71.0    121.0\n",
       "Walks    263.0    41.114068    21.718056   0.0   23.0    37.0    57.0    105.0\n",
       "Years    263.0     7.311787     4.793616   1.0    4.0     6.0    10.0     24.0\n",
       "CAtBat   263.0  2657.543726  2286.582929  19.0  842.5  1931.0  3890.5  14053.0\n",
       "CHits    263.0   722.186312   648.199644   4.0  212.0   516.0  1054.0   4256.0\n",
       "CHmRun   263.0    69.239544    82.197581   0.0   15.0    40.0    92.5    548.0\n",
       "CRuns    263.0   361.220532   331.198571   2.0  105.5   250.0   497.5   2165.0\n",
       "CRBI     263.0   330.418251   323.367668   3.0   95.0   230.0   424.5   1659.0\n",
       "CWalks   263.0   260.266160   264.055868   1.0   71.0   174.0   328.5   1566.0\n",
       "PutOuts  263.0   290.711027   279.934575   0.0  113.5   224.0   322.5   1377.0\n",
       "Assists  263.0   118.760456   145.080577   0.0    8.0    45.0   192.0    492.0\n",
       "Errors   263.0     8.593156     6.606574   0.0    3.0     7.0    13.0     32.0\n",
       "Salary   263.0   535.925882   451.118681  67.5  190.0   425.0   750.0   2460.0"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We will do **One Hot Encoding** to categorical columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>League_A</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_E</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_A</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   League_A  League_N  Division_E  Division_W  NewLeague_A  NewLeague_N\n",
       "1         0         1           0           1            0            1\n",
       "2         1         0           0           1            1            0\n",
       "3         0         1           1           0            0            1\n",
       "4         0         1           1           0            0            1\n",
       "5         1         0           0           1            1            0"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoded = pd.get_dummies(hts[[\"League\",\"Division\",\"NewLeague\"]])\n",
    "one_hot_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "new_hts = hts.drop([\"League\",\"Division\",\"NewLeague\",\"Salary\"],axis=1).astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat   Hits  HmRun  Runs   RBI  Walks  Years  CAtBat   CHits  CHmRun  \\\n",
       "1  315.0   81.0    7.0  24.0  38.0   39.0   14.0  3449.0   835.0    69.0   \n",
       "2  479.0  130.0   18.0  66.0  72.0   76.0    3.0  1624.0   457.0    63.0   \n",
       "3  496.0  141.0   20.0  65.0  78.0   37.0   11.0  5628.0  1575.0   225.0   \n",
       "4  321.0   87.0   10.0  39.0  42.0   30.0    2.0   396.0   101.0    12.0   \n",
       "5  594.0  169.0    4.0  74.0  51.0   35.0   11.0  4408.0  1133.0    19.0   \n",
       "\n",
       "   CRuns   CRBI  CWalks  PutOuts  Assists  Errors  League_N  Division_W  \\\n",
       "1  321.0  414.0   375.0    632.0     43.0    10.0         1           1   \n",
       "2  224.0  266.0   263.0    880.0     82.0    14.0         0           1   \n",
       "3  828.0  838.0   354.0    200.0     11.0     3.0         1           0   \n",
       "4   48.0   46.0    33.0    805.0     40.0     4.0         1           0   \n",
       "5  501.0  336.0   194.0    282.0    421.0    25.0         0           1   \n",
       "\n",
       "   NewLeague_N  \n",
       "1            1  \n",
       "2            0  \n",
       "3            1  \n",
       "4            1  \n",
       "5            0  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.concat([new_hts,one_hot_encoded[[\"League_N\",\"Division_W\",\"NewLeague_N\"]]],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y = hts.Salary # Target-dependent variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will split our dataset as train and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 20)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 19)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Independent Variables\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263,)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dependent Variables\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape:  (210, 19)\n",
      "X_test Shape:  (53, 19)\n",
      "y_train Shape:  (210,)\n",
      "y_test Shape:  (53,)\n"
     ]
    }
   ],
   "source": [
    "X_train = X.iloc[:210]\n",
    "X_test = X.iloc[210:]\n",
    "y_train = y[:210]\n",
    "y_test = y[210:]\n",
    "\n",
    "print(\"X_train Shape: \",X_train.shape)\n",
    "print(\"X_test Shape: \",X_test.shape)\n",
    "print(\"y_train Shape: \",y_train.shape)\n",
    "print(\"y_test Shape: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ridge_model = Ridge(alpha=0.2).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.2)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -2.41189039,    8.91147063,    2.84201352,   -1.49426997,\n",
       "         -1.05226295,    5.6084127 ,    0.60105755,   -0.16107707,\n",
       "         -0.28338429,   -0.8960649 ,    1.88742486,    1.26712845,\n",
       "         -0.87338654,    0.41711639,    0.63419076,   -2.87772869,\n",
       "         18.86699972, -128.26622009,   50.77669259])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lambda_values= 10**np.linspace(5,-2,150)*0.5\n",
    "ridge_model = Ridge()\n",
    "coefficients = []\n",
    "\n",
    "for lam in lambda_values:\n",
    "    ridge_model.set_params(alpha=lam)\n",
    "    ridge_model.fit(X_train,y_train)\n",
    "    coefficients.append(ridge_model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50000.        , 44873.52004792, 40272.65602982, 36143.51675475,\n",
       "       32437.73647393, 29111.90835944, 26127.07606801, 23448.27743463,\n",
       "       21044.13495104, 18886.48823234])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda_values[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([-0.51724961,  2.20034745, -0.07801802,  0.99088638,  0.26928077,\n",
       "         1.57081143,  0.04971685, -0.33176435,  0.64136127, -0.06561597,\n",
       "         1.07674083,  0.88778441, -0.30484539,  0.42449213,  0.46254923,\n",
       "        -0.38833383,  0.05430532, -0.13718731,  0.05497141]),\n",
       " array([-0.58776623,  2.3690747 , -0.09175069,  1.03451326,  0.27434366,\n",
       "         1.68190105,  0.05564627, -0.32840644,  0.62274636, -0.08593127,\n",
       "         1.09578385,  0.89759135, -0.32073582,  0.42520274,  0.46880314,\n",
       "        -0.42381594,  0.06056795, -0.15220841,  0.06142064]),\n",
       " array([-0.66066079,  2.54674474, -0.10672988,  1.07557019,  0.27883687,\n",
       "         1.79682549,  0.0620108 , -0.32465588,  0.60280013, -0.10630167,\n",
       "         1.11510592,  0.90723351, -0.33701251,  0.42591673,  0.47500509,\n",
       "        -0.4615529 ,  0.06753149, -0.16885879,  0.06860893])]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefficients[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.plot(lambda_values,coefficients)\n",
    "ax.set_xscale(\"log\")\n",
    "\n",
    "plt.xlabel(\"Lambda Values\")\n",
    "plt.ylabel(\"Coefficients\")\n",
    "plt.title(\"Ridge Coefficients\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.005)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 403.22327979,  758.42456908, 1182.39938932,  650.65312842,\n",
       "        578.97168623,  190.89240803,  106.06933525,  193.04720572,\n",
       "        657.00170616,  968.69758572])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model.predict(X_train)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=ridge_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303.2289882531757"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Error\n",
    "np.sqrt(mean_squared_error(y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5752262814405376"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=ridge_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "336.9858128683326"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Error\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22249990795089036"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lambda_values= 10**np.linspace(5,-2,150)*0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50000.        , 44873.52004792, 40272.65602982, 36143.51675475,\n",
       "       32437.73647393, 29111.90835944, 26127.07606801, 23448.27743463,\n",
       "       21044.13495104, 18886.48823234])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda_values[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "Ridge_cv = RidgeCV(alphas=lambda_values,\n",
    "                   scoring=\"neg_mean_squared_error\",\n",
    "                   normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([5.00000000e+04, 4.48735200e+04, 4.02726560e+04, 3.61435168e+04,\n",
       "       3.24377365e+04, 2.91119084e+04, 2.61270761e+04, 2.34482774e+04,\n",
       "       2.10441350e+04, 1.88864882e+04, 1.69500642e+04, 1.52121809e+04,\n",
       "       1.36524821e+04, 1.22526986e+04, 1.09964343e+04, 9.86897429e+03,\n",
       "       8.85711232e+03, 7.94899614e+03, 7.13398876e+03, 6.40254375e+03,\n",
       "       5.74609351e+03, 5.15694884e+0...\n",
       "       4.84782781e-02, 4.35078197e-02, 3.90469804e-02, 3.50435091e-02,\n",
       "       3.14505122e-02, 2.82259038e-02, 2.53319132e-02, 2.27346423e-02,\n",
       "       2.04036685e-02, 1.83116886e-02, 1.64341985e-02, 1.47492067e-02,\n",
       "       1.32369765e-02, 1.18797946e-02, 1.06617640e-02, 9.56861760e-03,\n",
       "       8.58755108e-03, 7.70707291e-03, 6.91686981e-03, 6.20768593e-03,\n",
       "       5.57121438e-03, 5.00000000e-03]),\n",
       "        normalize=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ridge_cv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009568617603791272"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ridge_cv.alpha_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will create our last model with optimal alpha value that seems 0.009568617603791272."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ridge_tuned = Ridge(alpha=Ridge_cv.alpha_,normalize=True).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=ridge_tuned.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "306.6077508440482"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Error\n",
    "np.sqrt(mean_squared_error(y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5657073660567311"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=ridge_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "327.87513556679505"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Error\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2639722635189039"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Lasso Regression( L1 Regularization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Least Absolute Shrinkage and Selection Operator Regression (usually simply called Lasso Regression) is another regularized version of Linear Regression: just like Ridge Regression, it adds a regularization term to the cost function, but it uses the ℓ1 norm of the weight vector instead of half the square of the ℓ2 norm. This penalty allows some coefficient values to go to the value of zero, allowing input variables to be effectively removed from the model, providing a type of automatic feature selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This is the cost function of Lasso regression:"
   ]
  },
  {
   "attachments": {
    "Screen%20Shot%202021-07-18%20at%2020.39.59.png": {
     "image/png": 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4aWY6VXNlckNvbW1lbnQ+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+Cvk1XPoAAAAcaURPVAAAAAIAAAAAAAAAWAAAACgAAABYAAAAWAAAYWP+vgliAABAAElEQVR4AezdB6BlVXU+8ANDM6DYG6hvFCmKHQtYMhYU7DFRUWOJJsTEmGLiP4nGaKLG2BI19sQWBFtswVgRsCA2REUUxQjYQImgiEjnv3/r3u+9M483MCP3zczDvd4795zd1l577X3O/s7a5WxxSaOhU9dA10DXQNdA10DXQNdA10DXwIw0sEUHmDPSZGfTNdA10DXQNdA10DXQNdA1UBroALM3hK6BroGuga6BroGuga6BroGZaqADzJmqszPrGuga6BroGuga6BroGuga6ACzt4Guga6BroGuga6BroGuga6BmWqgA8yZqrMz6xroGugaWPkamF/72daAXtyOLbfccthiiy2qYBdffHGd+Y2v11VqvJI2fOOHI/4hPIXlGOebOP3cNdA1sDI00AHmyqinLmXXQNdA18BG0QBwF+B4SQOTF1500bD11lvPg8zzzz+/AOC22247XHDBBXUtPCCSkHiEXAtz4Jtjq622qigXNf6Jg8+FF144xI971apVYdXPXQNdAytIAx1grqDK6qJ2DXQNdA0stwYAQCBvFavl1KIYSyIg6AAAxQESxWvocR5gVhxAchoXQAQupQlYlEdAZ8ojHX95jUm8xX7j8H7dNdA1sHlqoAPMzbNeulRdA10DXQObRAMFIBt4BC4zhD0GgwGYAGMsmxF0DE6BTm4gNAAzfJzxQcCjeNwOboc4/BOnLvpP10DXwIrRQAeYK6aquqBdA10DXQMbTwPAHivlueeeO7FUNkskC6QhcuBvu+22K6skifgDnMLOOeecYYcddhi22WabAozihs4777y6lHYMKi9sQ+0ArWH3gMpxuvF1ePVz10DXwOatgQ4wN+/66dJ1DXQNdA1sVA3EkihTYG+poe1YGUswlsephOKbl8lqGcskkAogAo94oQDSXIuDYrl0TQ7pcvDr1DXQNbByNNAB5sqpqy5p10DXQNfAsmsgoDJgL0DPGUAMuAT8Akad+SPphSH+ACfKQqAARryEA5vOoXHaxI1f4vRz10DXwOavgQ4wN/866hJ2DXQNdA1sNA0Afoa6DXEDf4iFMWAw1kbhrsdWSUAQwJQu4FB6cQy1s2KybuLFz+E6oNWZn7Tideoa6BpYuRroAHPl1l2XvGuga6BrYOYaAPiARGDPGYgEGLkBvwBMAPAHP/jB8O1vf3v4yle+MpxwwgnD6aefXoDxzne+85Aj6aRFP/3pT4eTTjpp+OY3v1lppJPnb/zGbwzXve51h3ve857DbW5zm+GGN7zhPEhN2pkXtjPsGugaWDYNdIC5bKrtjLsGuga6BlaeBuYBJmtkA34AJTAJ5AlznHXWWcN3v/vd4Zhjjhm+/OUvD8cdd9zwjW98Y/jxj39c8e54xzsO97vf/YZHPepRw/Wud71h++23L0VIc/zxxw9HH310Acxvfetbw4knnlhWy6tc5SrzAPPe9773cJ/73Ge46lWvWouJgNROXQNdAytLAx1grqz66tJ2DXQNdA0sqwYMUWeIHLADKNHF/Nt8SnMpgcmDDjpo+NCHPlQAEQA11zJD3iyet7/97YcDDzxw+M3f/M1hp512Gn7xi18M733ve4cPfOADw8c+9rH5uZniArHyydD63e9+9+Hv//7vh9122224znWuMz+/c1kL3pl3DXQNzFQDHWDOVJ2dWddA10DXwMrWAKBnaNxXfJo5cmEeZgOQv2zzKIFKAPHTn/70cKMb3Wi4xS1uMey1114FNL/whS8Mhx12WKVfvXr1sN9++w0HPPKRwzZt7uW73/3uGhIHWm91q1sNN25pr9KGxc8+++zhgx/8YFlDTzvttAKaN7vZzSrt4x//+AKqfYh8ZbepLv2vpwY6wPz1rPde6q6BK5UGgKKAEOAoFL+c+Y/juo5bHNehsXucPuGb+jyWlSxLyZg461OWxM158WIbVkb7WL7qVa8ajjjiiBomByBZKG9729vWPMz//u//Hl7xileUdXLnnXce7nGPewz3ute9Kt3BBx9clkyWzX333Xe4yU1uUvMuf/7znw9vf/vbh0MPPXQ48sgjywp6tatdrcL/+Z//edh///03taqvHPlX015o363F1P+Vo3C9FJujBjrA3BxrpcvUNdA1sN4aAIiyKMV1hmmBKkOuyPKSfPZQHGGOLGJZPMdQGEvbxRdb5TzZt7EYbUY/ZWVswikPWVOmsYjRBT0Y4m6R5nUinrQh14t54usIifPGN75x+M53vjPsueeewz777DPc+MY3Lj7mU77vfe8bnvOc59QQ+9zcXM3D3LoNn//0Zz8rC+df/uVfzs+tjMx4H3vsscN73vOe4cUvfnGl5ac8b33rW4cDDjigrvl1WloDqcXJMqq146SOheV6EqP5TBPQ9WK6LJ6L43Z318BSGugAcymtdL+uga6BFaMBnabDvEFnW+GEuIEmFLDpetyhLo5jaNjilsRxHoMs6TclkdcR+ciSMjijgLe4+Yk/TsNvMYnvEC/XcePpAC7Np7z61a8+XOta1yorpDgW+5hj+dKXvrTqYscddxx22WWX2u5o1113HR7wgAeUpdN8TPM46Zm0eJ7UeL6npX3mM585PzeTDADnI9sQe1aUL5a3uycaCBjkWgwV1U3863oaudpCi8y5VLu4LJ7FsP90DVyOBjrAvBwF9eCuga6BzVsDOk0gEsBE9mcEWpAwVjw0Bpjc6VSTPnFi9ROOj3PiirOpKfJGJm7knOvIPI4TndBVwpPOWXjAuGtD4ovjcgsLL+mQfI/+zGcKJL7mNa8pkKgeDHXf4Q53KEvmYx/72FpNHmtx6oUsti5i/fyjP/qj+W2Q8GUNfcQjHlGLfVIW/p3W1sBlgcH5NiFJi9haSSVurbrQKNdSur0snsWg/3QNXI4GOsC8HAX14K6BroHNWwNADzAUAOkaiIk7wCuginup+MITpsQBQkt1vptSI2RUZkesrcrqAPxSXuFjHaR84085KkeAnrjSINc2Rke+G46ndL4zbjshuqn8m38LLIvv4YcfXsPcb37zm4snHqzJz33uc4ff+Z3fqfmXxbD9REZu11u1uO9v8zeBSXUT+od/+IeyYLKAbm71EBk3h/NlgUH6RWXZHEecevJaSrfjqIutopJ26hq4PA10gHl5GurhXQNdA5u1BgCdAEadqYUogA2Ak87V2QGABWhJF5AUQJp4CizeGHxtbkqIrM6Rl8wpE/+A5PgBCuaiRgf05ppe6CvgjjuW3FXt2pQB/MQVL+kr4+Yv/N///d9rpbg9LtWBYW3bDT3xiU+sTdcBVXwR3qkLsp155pllwXzKU54yr3PxbFUEdO6+++6VJ79Ol9bA5YLBVj9F44g8qkEsDR/HUZeOMWHZf7sG1qWBDjDXpZnu3zXQNbAiNGCbm+9973vDKaecMvzkJz8pgBIQlAIEjAFGAUksciyAq5o1LrQWcGqewA8KMCrHZvJDVpSyuR77XZZbmHTKJw2r5DWucY0Ccr6mM6axDlatYiGdpBVnrJe//du/Hd71rncNp556avG93e1uNzz5yU8ukGnFuPwSH091hIDN73//+zV/86//+q/XApgvfOELh0c2gLlz29IoZatE/WctDVwuGKwIKm6tZB1gLlJHd85WAx1gzlafnVvXQNfARtbAySefXPso/ud//ufwxS9+sYZ0zcdkjWPBGwMw14AK/1jvWNZY3LgBIOBHPBQLYCx7G7lo68wu5VAWR9xAW8C1cpDbWRz+4gHWSFmVD8C79rWvXavCgUSfaswwuniurQRn+cQr+sHLYp2Axt/93d8dDjnkkMprhx12qHmXAKKN0n0GMnImb/CY5VP+Ph1pDubf/d3flczioDe1FesHtK8BjRduTUL671gDY9y4pLWxIrSfcUQMRG5tYykaR106xlKpul/XwIIGOsBc0EW/6hroGliBGvjf//3fmvvHemYlM+CTQ3HGgItbGAo4GoMwYAngCZACwBC/pCuPTfgTsBigRxR+ofF19BA/51wnTJmBQNsOsSDeq+1bKUyZ6cYBhNOFPKUv/bRw1l8ryn0C8hnPeEZtwi6tz0M+6EEPGuxjCVxGj2OZw0vYpz71qarD1772tZWv/KxQf8lLXjI8+MEP7gAzlbuO83qBwXGkMZ+FpjP2XQuLriPKWvG7o2tgsQY6wFyske7uGugaWFEaMLz68Y9/vPZn/ExbyQzgIGcgBoAJKAKmAp5YxbgtZgnw4g4IEm8cf3NRCpmUCfAjY2QnH1CI+KUs5dF+EncMuMUXz7B4AKbvgEt/AStwC5cuK/PpRnwUgGhI/POf//zwb//2b8NRRx1V8c2ZfOhDHzo861nPmgeXSVNyNMdFU7CK9zve8Y6av8mKSSaA1xeC/uZv/qYAbwAqHp2W1oBWP2sguBw8l5a++14ZNdAB5pWxVnuZugZ+jTRw1llnDYbJn/3sZ9d3rlkmgS9ABZhxhAI2MwQMLCUcqCrw086uM6QMSDk2FyKzMioDeZWJHxn5kd3Bn3ush5QvZROGDJH7fKMh8vvc5z6Vng4RPuJFD9LKTx7inHjiiTVFwdd4vvKVr1QaX/B52MMeVt8ilx5JH9mckzd+wOk73/nOeYCaT0X+3u/9Xm1zVAz6T9dA18CK0kAHmCuqurqwXQNdA4s1AEQZpn3b295W8/g+8YlPzFv3gBoAB6ARD5hxja55zWvWd65t5G3OYICQsDHoFD9uYZuayOMYAzTy5RD2y1/+cvjRj35U3/7+6le/WhbGyC1dAGJ4sGDe5ja3mR8iFzf86NBK8pZpWFT+pZcWdmyblvC6171uoHebsAOdj2rzJm1NZHg7vAJquaXNSwAZfOHHt8rPOOOMCvM5yQMPPLA+Q7l69eq16qYY9p+uga6BzV4DHWBu9lXUBewa6Bq4LA0AQkDQ1447bvjwRz5SYOf000+vPRvHABOgAWxy+BLNXe961+HP//zP51dPA15rAasRIL0sGTZ2WGSU71jmlM1+lT/+8Y+Hb3/72zUv1RxHQPOHP/zhvEVTWvqRBsD0PfGnP/3pNSQtLHmII4+A0viLIy3eFufI6//+7/8KyD/1qU+t7YV8SlJ8FDnL0X7wtcG6BT7Sf+xjH6sXAFMXzANlkbb6XD2NwX/S9/PaGig9N11bjIVST2kT/Ogcjf24U0f8Q/HjHvsnfMWcW/Nrr19t+kAr27R4F13U2n0rwJZtVwSkidpRYostxZrEEwdNdk7Qhj07JnFxjFucTktroAPMpfXSfbsGugZWiAZ0hKxgOsEvfelLw4te9KKy2Nm6CKWjTKfK7eA2FGsxysMf/vCyZgI3CR8DqxWiiiXFPPvnPx++/4Mf1J6SH/rQh8pyGOth9GBRDgvmX/3VX9UqctZGcYS7znfMAT1+dEPnwvB8whOeUFZkVmJhPvlo/0rzKAFLFHATIenf98t9g9wczK997WvFz/zL3/qt3xpe/vKXtbTTb8nr2TtdpgbUF1IntGWFPj/uvByoH/XHXfGmeo0lO/HEyRGeziuRLrm4PR9am1zV2tIWmmLDhued17761ZS07bbblFv4hRdeVBv+V3ttYXaWEGm77bYZLrqwgc3mB2wCnk09BTYn+lqJWtk4MneAuXH03HPpGugaWCYNjDtCFjQryV/5ylfWwh/bFQE24gA0zihgc/vtt68Vz/ZrfOADHzisXj0ZjhVPZwxQ6YhXMimHKQSGoN///vcPH/7why8F9oA6czD/3//7f2U91HEGYNIBHUaP88Ck6egnbUgbzz/7sz+bdsgTgGNxDtAOYKZ+pMs1/asrX//xQmAVOmumcNsdGV7ff//9S+3irvQ6WO72E73mrK4W600YkFQ2uimwFG8MLvMCET65T5xXLLViK/rkzlcK4JnfJOD8dn8oH7DpfHEDpNr+lqyZDZEW4JwWXpoF3bZ5yPxXsGqmxVq2UweYy6bazrhroGtgY2hg/NC/qHUM57T5h//xH/9RlrFjjjmmOgtyTDqPCdgcdxr8H/CAB9R8QSDT97MtotHxipdjY5RlOfKgH2X55je/WYugXvziFxfgjMVLuEU+t7zlLWuRj1XkKMBjHmA2HlaVj0H38ccfX3p+/vOfX/Hpkr4sFgIwrSbHH5nH6UoHLW9zNq0af3P7tCQ/82B9/QdYvd/97jdc//rXrzpb6fqvwi/jD/0a3qUn+lVvCyBo1XDaaacNpzcwf0b7CAFSR4bRb3CDG9RWUFe76lXLfy09N54soAVGC2itbBSlCTr80NdYVxcCmK2MW2+1dX0k4Mdtes1P25elMtWAcm584xvXPbJDeyHFZkvtfBUwOsGXZRkVsdNaGugAcy11dEfXQNfAStOADkOnGnDjDEz9d/u2te9g55vaOhXAJp2vdOICk/ZdvNvd7lbz/ubm5ubn/YmLVrIFTTmVQ/mPOOKIWlDDYmj1PaI7e04CmOZCAphJQz/KLn0OABMv9NGPfnT4r//6rwKJ5dF+8DNEDmDutttuFRc8AVikM/TIWmmPTHVkXqh8xLW10W//9m+XLGSQd/IK/35eWwOGgC++2MuQIeAJEEz9ae+f/OQnh89+7rNt+six7QVhMowOTO233/2Gffbep7anwlEadUfnuIQHCLVqq5VrxW/FqrJoY9Ch4fD5NtUC6YwOz2ujHdrzF7/wheHENp/Y6IcXVmV/yEMeMux9l72HXW5+89ZWgXQKGuqFi+62WsH6If9yUQeYy6XZzrdroGtgo2gA8NGRBoxwW+RiqPyggw4ajjzyyFrdDBiNAaZORqejI3XNSrHvvvsOj3vc44a99tprvrOtDrd6lI1SnGXJRBkdLI4HH3xwDZOfcMIJpQ8ZWuTj047mYK5Zs6bAZHXIU2notDFoVpuFOZHCWR9tL6Rjjj6BFIunWIMNd/sEJQIqLb6yldEHP/jB4dhjj62hcSvezf+8733vOzz+8Y8vK2ZZ1Rp/+cpHHXRahwaY1NSvv3Yuy+OovR522GGD/WGt9reohT4d+++3/7D33nsPt9hjj9KztNE1y2W1GfXewFTqfR0SbNbek+cD4Nx2k2iSTiyzE6DJeqlsaV8fp6ujj642GhC6VQt/SHvxuctd7jKsXr16qr8JyLz4oqZ8+pkC+81aEZtAuA4wN4HSe5ZdA10Ds9OADqQsLw0ktqd/6xhZKFbVKmqfjnzLW95Sw7EAjnjpSNPRkoS/L87stNNONUR7//vfvzb7tgn4xDqha1q5VMCj6cmm6KYN2Hfy05/+9DywMAfz1re+dc3B9KnI6EiJo1/6ytSBhJs/aYGOFeoBmOJnX81MOcDn522xEYD59a9/vSypgKU5sHNzc7X3pv03s8k7/givgJ7y6D+X1kCpagIuq56nuqM3dWJ1/lGfOaq9cH1lAjALZg3D/vfff9inWeV236NNY2gWPCRNnet38sNvPFw8CloRl+ZUur+3NI6tfPTTztqWF87oSWFY+I+egvEqd/MDQB/60Ic0gLn3cNOb3nQ+fYFKamssp2rDotNIAx1gjpTRL7sGugZWpgZ0rAElzrE+6Fhe//rXFwj67Gc/Wx1NOhSdCwKaXIuLDjjggFpkYh6gofOVbsGkj5Ttl82yaz6efSc/8IEP1CcYLQIy73TXXXetKQIsidLQU9IG6I11JcyiIBZMWyJx0zt9iR9KWhbkyCHMin0Li+j7Xm1bIvnTN0rnPx6Or4D+c2kNNL1XfRXSacGt3tK21YWvXB3NKtdeAuj/Yi9gDWzd/wH3b0Pkew977L5H1bX6RtoD/ZtnuBWr33QrnwpcqT8NCLJctv9pWQsXVmkuMsWg6UW5zQv+fBsiP77taGDxD31p0/ZzteXWbrvt2vwmbZv/ZCHQSlXK8svdAeby67jn0DXQNbARNBCQqKMETJx1EPnSDGsbK2Y6X50yCihybd4VKyZrmuFiw+bA10qnlFXZWQ7tMWlVuc3Y6YgF0zA1wMiCGXBZwKXpEeDIdfR09tlnD3/xF39RC3XwcMzNzdVQty2iTjnllPkhcJ03sqflzjvvPOzRhmXN+bTK3GFBj0U+8pUXmnTgkyHd8ug/S2tgCjDLCtkwIotb6luCApifPbpZmY8bLriwgUd7QDY9P/ghDx7uus9dCzSNLZhJC26OeS6d+ebvO2/BbIDQqvBW9AYmbcE1Gemojwi0YlzQ7o1PNoB5dHsRNX1jspBn1bBNewG97/3uO9zpTndqL0G7VYHpBqPJPaGtlk+F9Z8FDXSAuaCLftU10DWwgjUAPAWcxOrIMsESYX/MV73qVWXJAXx0sDmkYZnjDg8LTh7z6Ec3K88DahGEofKVSjpB4A9gQ8poiPxd73rXYAqB8tsH07zTpz3tacOaNWtKF9GldHhwO+g2n+e0KOgjbXN7fkA9K48tin7Q9t0EMn0n3qIeeaIdd9yxVi/fvC2WoGOrxg2TR//JQ13gx+06sheT/rO2BrwnTevY5XiepXr7WJtXyIJpGsNFbTFQdPqQsso1gNksx9JPXrfwKuzU9O7zoxOr/sqeg8lCOWlHhvpbc2ovktPPrLZ2q+2ismBaEDUFmCCjtqdt3m+//YY73/nO1Wbpj74wqjgNXHaAWSq81E8HmJdSSffoGugaWKkaCBDSMSDAht+ZbdsRK8tZMQEigCugRscivgOokQagMSfzec97Xg3hWmUdnitNN8oD5BmSVi7ltfKbBdMqbrpgQfR5RkPnaxYBzHTAOla86MjKbwtHXvGKV9T3w+nEpzdtkP6a17ymdCU+3UvPSqRzlxd/B/0LZzXGk2x0PAE1k7oQJg7ZO12GBhreseLZ8HdTYumcPunuIx/9SNWVIfJq59OhdFtzWUW+ewP6adtVN62OuNVJY1b/l5Hzigm6yIKcRsBgfcmnFc3G6ag1x+GC1tZqiPzznx+Oa0Pk7pk8G+xuYJeJ3dsQuU3asxKdddN2RZ2W1kAHmEvrpft2DXQNrBAN6EQd7ac6V2KXu53Tceosfvaznw2HHHJIbQzOogPsjElc6ZJGB22zb9vm+LY2QLQSyR6J9q8E9JQNiLDZOoBplT2yitwin3zJJzoQFl26RtxWoFvcYx/Lb3zjGwVGpLedCytoQGnO8gw45YfHmG/yc66wFh8c4Haoi05LayD6KntawzrRm9jCDJFbGW1XBWHI8O8DHviA2npnAjAn+jVsLA3IZKEcN4SZ7Y+kXalUK76b8Mpi2LzKaC/LBjzpxXSAw484vMD4l4/9cm1P5N4xd/PBD35Qs87fte3rag5mi01HTYftx38dK1Uvyyl3B5jLqd3Ou2uga2D5NdAe9jqBUEAKd4CJThOgNLfKFjkW/pzRvkITC1l1Fi1+da56jOm1OZj3agtQfFt7bm6yP+aYv3jjtGO3682FADxyOlwbBvSJRhZIbgDTnEjD28qL6Is+AhJTFn6G1qVlxTQUDnxbMc6C6Ss80Qn9J08AU7zwky8SB08krmuHcHHDqyL0n0tpoPTVdDVp601/8zBy0p4PP/yIApjHHvul+RA6VV8W+ZiqEHA6QVoFt+brol1cKeoAqERpYynzZP7ppN19vH1Z6jNHHTV8qelq/oWytcUHts/JTnS164IOG688dZqKOi2hgQ4wl1BK9+oa6Bq48moAKLIROCuczxXGkqnjcYyBD7cO2He1fbPcqmdDh4vT0JZ04eG8OREQUhI1uVz7BrhPPP7TP/1TgexstJ5PRZLdXqLimiqAXCs3IPOpT31qeNaznlU6/En7Qozy/vEf/3HpyR6Y4tIHUGn+akBk9JJzMe4/V1gD9F06pffpNXe7HAo0tTZvHnJIWAHMNmd2990nC1egpeZdhJ+DpXPeM4lX6Jku0EIZF14oFfvCZpn06dKjPzOx9k4iX1LfJ39AA+N7732X2ulgIf1U5xWx/yylgQ4wl9JK9+sa6Bq40mrAp/NY8Cz6AZRYyVgyA55ScB0ssgjFynLgy3YlNg4HnoQDm4k3Bk3j6/DblGcyBhST2Qpwe2IC2coCBGarImBT/PEBIE4sZEPNTfvQhz40POUpT6m5rXSnvPTj6z177rnnvJVSmfF3yF8+k7l9m1IbV7681ZU6SJ25djDalQXzMgHmxCoHZE3x5aX4XBk0RjeIXhBn9LZOgNni2Qe3phMUwPQln6TvALMUeRk/HWBehnJ6UNdA18CVTwMAkVXQhslZ8Wz8bY4mAJSO2VnnE3BEC74yw5JpG58M3Y6tmQBYOp+cNwftpRzjDjbl4xd/Zco1uWN1HOuFn62NzL30zXCLIBL3T//0T2t43BeB8MohPUumw2KdDjBLZTP9UW/jOnXt6ABzQc1p2/SCOsBc0M1yXXWAuVya7Xy7BroGNksN6GgcrHdWUT//+c8vgElYYAiIcgBIABQ/8W3ls1/brsSw8tXbdjvbTMESwAqIBlClc99cCk825cicsqyMJSeZlQ8JBwKVNXH5c+PhLP5xxx1XC4ToYawvcy8t8vE1nm2bpXLrdgCU8hEvMtBtOnn8O11xDagbOnXONXcHmAu6pReUtsc5r6vmv+QQefPvFkxa+9WoA8xfTW89VddA18AK1QCwBEjZcNx3sd/0pjfVUPlJJ51UHQ4ANCYdUsDjLrvs0j4b99DhYQ97WM3HTIcufoBTOrAxj015TUZlJl/KrjyRN2WYgEWWrwnATjmEh/j5ApBtjqzID/AUbk9L2x35TOQTnvCE2tbFcLu8wit5xh2+/XzFNKCO6DR16drRAeaCXtOO0/Y6wFzQzXJddYC5XJrtfLsGugY2Sw3oaABMHY1PHFoR/YY3vKH2wPO97JB4AFEOaa561asOQOaTn/zk+mKN1deGfMUZUzqxsd+mvE7nGkDITUbgD/FnYUxZuAMGhYsrjcMWT19on9NjyQRKw0s81/xMJbAxtS/3SIuXvPBFybcc/ecKayB14Jxreu8Ac0G19ILoBXHO66q5uwWz1DLTnw4wZ6rOzqxroGtgpWjAvEsdjGHcF7zgBcPb3va22owdkEQBjeKkI+Kvg7Ivpv0x99133/p+dsAYcIU2NwCVMpCNrOeee271sNu2b38njOyZH2nYXxlSLmUWj26ARG4LdoBSZEidHx78pJPeMdYJvkjaTrPTQNpn6lJdODrAXNAx3SB6QZzRG58OMEstM/3pAHOm6uzMuga6BlaKBsYAkxXTfEyfUDR0ruMJMFIebqDJgfK98qc//en16cNs5SNMB5Z43JuaUpaADucx0ItVMeHkVXZl4Iec8XG0nwZcJmAzgFR8YeLxC/+rXOUq5S+P6BPfpCvm/ecKayC6Tx2lLjvAXFBttd3mTJueNOVJm+0Ac0FPs7zqAHOW2uy8uga6BlaMBljjdDqsbIbGDfu++tWvrs3YfUs7lsx0SOm0U0Abkz/pSU+qVeW+rQ00JU7SJO6mPCtjrI7kcM2PjLEwcgN+AZVJI7543Lm2NyKAyVIpPQrAlJ51EniXD51UnBbf14SSh3On2Wkg9emca/XWAeaCjsdtmK8mPa+r5u4WzAVdzeqqA8xZabLz6RroGlgxGkjH4pxrm65b9POv//qvw2GHHVZWOJ10jgCzFNK+katXr67PK/qus/mGIWk2J6oyNoHK8jida+nb4Pwz7E1eQ90BjayQwoHEgPEAxpQvwDI6ypmupAFCAc7wTPjmpJsrgyxpw865pusOMBdql14QvSDOeV01dweYpZaZ/nSAOVN1dmZdA10Dm7sGdCoA0Bjs8AOo7I958MEH1z6PvvgDQCVeOihnFjhga7s2h9Gqcp9IBDLHQGqz0YPytiPyK8/4mpzcdEJ+ZeOO31hX0UXOGfaOO2m4J4TPpFMP38SdRuinGWiA3uk19RYdd4C5oFy6QWmbnNFbHyJf0NMsrzYpwEyFV6XXT94sFhpCbphfdUhF+sUPyOSbGzIKTcMbxxfGLY2Hb9KWuPWTB+mksQrPzT2OK2poHIcfdw7lXJdc4/SuI2/8cx7nmzhL+Y3LNU6bNGM/1+VPn9My8sMjMrtuBRGx/ISvi1Le5JWz+GNZx+nFWUrmcZx+vaCB6Fj9VN20oHz6TRhwIMyRuFJzI36pX+5xfO6VSimX9pR2F6DkHj/mmGOGQw89dHjta187/OxnP5tftCIdir7SFvfYY49aUW5lua16dthhh3m+0WvyCg98krfr5abIIR/ypz2M5RJn7HYt3rrqPeF4jtNVfLpt+YzjRG+J79xpNhpI3aWeUx+zApiafqvSerz7cSdwywfVrTGNtNAjilTBk3DO5q640+sKbcxwxIv842dOypV8Jtx+tV+8UHhxzvNv/t2CWeqZ6c8mBZh5QKvwaofTxhp/D/u6bg+5VdP5TRtSeo3HMV7V6CHnAcg/gNE1SucrnEziOp/vKx9NBqtNkfgl81Te8pz6GxbCtx7i8mn+47jS5gYKf/mNZUq5wzdycuMlPZJ+MYU//3XlG396wcsQFr+kjVx48HOgxIl+uJWXJUca/pFtvFFzJZ7+hEfy4p38EnbxxVbiTjp//PjnILO0kXnKtp+W0MC4TWWIc7LaF1i8uFYS06P6o+fo2sbC9M/Nqiec/q08TnzulUpp0ykD90UXmY+5sBraJyR9+vDb3/728NOf/rTaKB1KQwfa4fg+uM1tblNbF61Zs2a42c1uVvGTj3juYWnpFCXvxeeVqtMu96bVgLamLaXNuXbMAmB6FoS/Du0iz+d23mJLc3inL6OtWfPPs3ysjSZGu1cmfciqVe6ByfWWLT3C2/MIL/fHhe3eskk/GvenKR9/1xtK8kFJy5ly4dYBZqlnpj+bBGCq1HRmSuM6DVODyrUHs+u417fk+F3QOkag1IMdT6RhcY+pGl2Tx9u2eNIGMAnjlwbpWvp0FvEnZ4isxXPqMY6TsgiPv+scAC4gy51OHRtx5R3/xE9ZyAwIcEdX0vAvUNDmVW3VDun4J2/hDmnCHwCJv/gpr2t5CAO0U85xJxu+4kZu58gW+ZIXtzTij+tamsU8uBe/KIjXad0aoLMc6o2u6ZwffcdPXfLnFiZe6jJ+/Ctei+teWcmkLMqFUlZ+jtzL5mN+9atfrVXlhx9+eLW9pMm9Kb62jK55zWsOu++++3DggQfW1kX2x0z8xbqlR2EWwriX8OvUNXBFNKAtastpx2nXVxxgtv6stdULqu+bGBJaNgUw5ZXRrEl+a5egBVfEybnda5zTtE3Yuixek1jz9wteY0q5cj+5fxwbSuRF4c8ZvcmxA8xSz0x/NhnA9GBOQ0nDUdke8B7IjotaHJ2ZeGkU61N6/GJ52UrHOeWL//hhPm5w0jj4xfoWd/LH0zXZEJkcZHa4lhYPh7jKiY/r8EkZEi98+CdPefBH4vFvFwWax+kSHn2WbPJvAdIAZWRKucdp8Y+b/K63Zs2SfnqM5a6VoS2et8vIF3nxEjfy8I+bDAl3rrK0iNGHvPglDv/IM44jf+VwiNtp3RqgUzqKbqNz9Za6TerofbF/wlM30l6snbSAtKfEWVFn7W3a5sg9bsvaHXJ2v7/uda+rr9Z86Utfmr/Hx/py33G7x2zJ43OJ5mTe9773LT5+WDzzHOBOvcijt2ca6XRFNTC+33Otnc0EYLZ75cILF6yT1f7b47d5l0Fk4m59YTBfAbdJicjiXptYK6eJps/uSy6ZPPOnMQvItpuj7sc8r/BG42dT5TftayZp1+8XD7TAc8KXWy4dYJZ6ZvqzWQBMFewBXA/31ghieRw3Kp3A+pJ0eAEnDpQHfNziaMTpXAIePfDFkV54wCYe5Ew6YeHvGn9hLBJpwNL84he/qDDzsoQ7wie84g4Q48Yz8Z3lRVbn8PdmCYDzD+gSl+zkEU/nFuKOnse85CUs8qRc/PDiL754QL+32bGeoivpxtfS5wgfcQJ48UP88BenZVb1P5ZTGAIwE1f+nZbWwLge6R9pW/RNl6mTnMVHOfMXl9uRNK7VgTMwFd6VeAX+0EnKp12lPPy0Rc+Eb37zm8P//M//DC960YtqNEAYin5c048DD3MwLfZ57nOfWwuA8NRWxc+BN38LhPiFR130n66BX0ED2qU25Zxr7lkAzMamPRyASby11wYCW1uHytwO9YRpP3WeRJ3486h07blT91c5Kx65JvdBiyNayT/pC9xHuUdyLY57TDykbBtKi9NiNa+rxqwDzA3V6OXH3yQAk1gqNg/4MVhIw5ncLJPGHL/LL85CDPwD+oAsbo3WEXAktg6Tn4f9fD4t7qQZTxr1uDNJHGfp8I38rvmPOw1xuIEq4Q4knuHrH/7wh8PJJ588nHLKKXX9k5/8ZDjjjDOGM888cz5u+F2tfaZutzYMZzuUa1zjGsONbnSjYW5ubrje9a5X/MdAYCznOM/wcsPHUildy6zcwunNeTwUzs9bqO8UN9GbzKeU3D/60Y8GewYqx+mnn177CeqY8zDwgKBbMup8HTe+8Y3rIH90R09kHstNT+TAg3/057pkFqHTkhqgq9Rj2oWI9Jm6ER6d8xMmXepkLcb4tTpC6mOl6z9tKWft350ZfVRB249V5ccee+zwn//5n/PfKxdGB9FD9Cyttn67291u+P3f//3h7ne/+3CTm9yknhPhK030HB0mLHn2c9fAhmpAO9aO0p7TpmYBMPHUxtup8tB3bNnmS07xY2vP035tKgOjRxNlIbzNr6xpNS1cnxNwOt/JFiPPl/Y8agVnYAopR+4X906I/4aScqCk5YzecOsAs9Qz059NCjAnjXbBQpKSaQBWb9qTDoA5++yzE7TeZw2GpU3jBO7wHDewNDIgSqfg7DvD9rbbcccda04Vt3gBkosbeOSPlRB/hzxROpD4h5dNnS0ecACWJzeA+b3vfa++iwxYKruOLfLmzAq6evXqkpOMO++88zA3Nzfc9KY3rYUFN7jBDQp4Ki9ZpSO7uZ1ucG4ypOyRk6wpmzgAorB5ayw+zW1eGiBJVpad73znO+WnjnzTWbivoMQSiwcdsHZd+9rXHq5znesU0CQn2ZXFggiA81rXutY8sJGePNKSNXIG+JAx8pK909oaoB/W7cznpUP6chaGnOmVn4OuJ+18aqGY6p7fYsJrpeu/dERP7Zh0LguW+iz2i6607/H3yvM8GuuNLhPfy9Qd73jH4fd+7/eGu93tbnW/RmdjvYnPjU+nroErooFqx9P7O9fa1SwBZntSVD8yacOeI54d7VnSMomxQp78xHWhZdcTx72mgO43hooKmT6HmlWUzKyjeHsWje+nuj9auOeZsF/1npEHyv3GOa+r5t8BZqlnpj+bBGCq1HRuGksqPv78TjzxxOH5z39+WQ2AsA0ljQi/8ObWOIEmACYgCDhjVSPP3NzcsOuuu9ax5557DqtXr54MMePTBABwwhO/dL78pc9NkRsgZWyJCuxKa8j8pJNOGt761rcOH/zgB9cCZZE1ZV24ESYgHN8JCJjcKMoDdO6yyy417+sud7lLLTQA5MSVv3ICwOK6JqsONF/VkFf8A+CShzRAdNxHH3308NGPfrS2cAE0dbTCyOkgf2SWNyJHdI4XnsLIBFQ+6EEPGh74wAcO++yzT3XEeJxzzjmVjjzAPyuzNFeGYdlSyjL/0FV0rT7Mm2wVU3WTdhA/9TeuK6Lxo3d152VD+92y1Zs6W/xStcxFWVb22r325oUs5crLKJ3kvqAH8V74whcOb3/724dvfetb8y9h9KudOpeuRhL/xV/8xfDIRz5yuPWtb115CMI38em5hg4b/05dA1dEA3n2Oue67v3WVRx++BGDPV3NIw4Jy3N39913rf4NGHSgBT6Ttnlxs0K6D1qyogubu0Wq50L8sjpcWu1c/Mmh3RfTApexeJLB7g2TZ1Cb29/uQ88nadxL/JH+Az8jfp5LuX8qcAN+yIXkizjnddXcHWCWWmb6s0kAppr1xrOYUvEa05e//OXhqU996nDCCScUkEkHsDjNUm58NNI0qPG1MP5pWEBLOlNn3xQG2nZqoPNWrWMAgObm5mpYWtocSc+NdEbk1gknv3Ty4rLsHd1u8k988pPD5z73ubJaGlLOd4/DBw/x8UCuHUuRNOJtv/32ZRk0HHeHO9yhOjXXrLH4hSIXd/JznTzjn3jkd1P/7//+by10MFTI6spSCQQKjwzSkjO8Uga8HJOHyOQNNGk8TFh7gHyAnq5ZftSDOImX8uPDr9PlaEA9tIPO6c4DeUzanJe2I488su6z73//+xWsDryk3Pve9677gL7HlPpVBzqDlUrKoe0uVT7+9JUwcR3IqnLfK/elHy+KiB6iF/oet89btP0x79/mYwKaRhzGccWTT3gnv2Laf7oGNlAD2pE25Zxr7llYMD3Tzz9/8llQ7RTfOsg4fRyXFbPF8yLagpsMU/DWomxZWxNNCmRnInM5kSlXk3iFPoun+49BAajUP+T+mKSe8mwZLH6mJfyyzuFFdjQvo/I0dweYpZaZ/mwagNmKMAY+SlSV32pcozrr52cNRx31mQKYOj9xNbw0kMvTQG6AxFtoUBMAxJ0bBU8Ht7O8XANtc3Nzw/777z/cd999h9vd/vbDDs1vcgNd2lqXt61tWofDlF985NPKY8gbOPvABz4wfPzjHy/rbKwn0skvb27c0ubmco0ibzoxZYjsztyG9IG1/fbbr1ax3r7JrBzCEZ74ceMXkpa/I7oRBxDxfWbbtOhYv/vd79bQvbRjPtLkiD938pUPfyRtyqJOxQPqWZFL12317Z3vfOcqC3mjH/FCySvufl6kgVZ3AGbqgh6jP3XiBQe41Ba/8Y1v1DQU/rvttlsBfCugAX0vKYvT4onXSgeYaVc0px3mfsj9xT2+X5Q598MrX/nKGjJnxU+ctO+xnnWS7kFbFxkqp096jg6TJxnG19ydugY2RAPalbbnnGvuWQBMQ9cXXNDuEfPvPb8bX8PczJ71jJkKKj9hjBJntKleZ5/98/Yids5wbutHzmOR1Ie3uOSzu8u2zZCw7bbbFJi8ylW2G65//esP27V75twGMD1f8qLn/kTcuT/ddxtK8kUL9+hEFu4OMDdUm+sXf5MAzNwE4xtBI9J4PJRPbnMSfQv47//+76vzqxtlClDWr1iXjpVGlZB0IGRI55BzOg1nloc//MM/HH73d3+3wFs61sgkjQO/HDoiZQGchLHCvvzlLx8+8YlP1LxFPIQ7hMuHH8sdN5ncTK4d8mIZdWY5SZyEi4+HcIfrP/mTPxke97jH1RxHWw+1gJIvaaMH8cedm3BuANgG0y94wQvq03nyjZVWfkg8cibPWG8jh/jqVfyU11k4GvNRfkP7e++99/D0pz+95AaYDZXIJ2mkcy3fTktrIPqO3ujcNVIf//iP/zgcdNBBtTiLLoWlLljvzZG1yTiLciwJ9K1d5cUo7XFpCVaGb+mp6eP81k7pIPehNidMGaOXtD+L8I477rjhZS972fCxj32sOlNxokfn4jtt5/bHNO3mGX/7t8N+7WU192niyDd1szK01qXcHDWQNuica21xFgCzsSm62Gbp7Xqy5ZCpNJP5+kClRT+rtpxMqTr1tNOGr33ta9PFnz8YTv3hqTUKCTh6/mAHXBphu8Y1rj5cq90j12n7xt797pOXsDxnyO/5w6KJPIPcK/x/FaIXlPSc87pq/t2CWeqZ6c8mAZhKkMoGaNIBqnj+Fvd86EMfGl71qlfVymR+4m0IpcOQRqNG8ZNfgFTylIeDG9gJMNLJ+BTcvs2K+bSnPa1AkDiRxzWSFrk5pBXuWmekI3rTm940sMYaWkaJL70Nmg1P3vzmN6/OXaeko09nJL6OzUKaU089tfTz9a9/vcC3soTwcuBtJev973//4Q/+4A9qgU0AQvKVJnHHuuAvDpDvm8zmiaoPnW74O9OlhTusjRbqWLTDTW4PBTwcysvSY0GQuWvAtnLwp1u6UgY8TVeYm5srC+zDH/7wGu73Nqw+xoAycpC109IaiP6dEZ1ZrGIe7Vve8pbhk22qxvhlRRx1Ss/qxQvVwx72sOGud71r6R8fdaVNOsRdyaQ8uYdTjrTrgGjtLiRM+XV22q451O9///tLn+N7MPHxpyM8vDha8ON75e4XOhbunhJOn526Bq6IBrQnbdQ519xXHGCmT9GPtud0E3KrrSftVZes7Wvn7glTvvR3plT9oPV1Z7XFrKyX5vtf7WpXHbbdpvUL7U/cc889r9r/dttNFtlu1yyY125z8r2MGT257nWv10bffqOVZaKVVpRWPs+cSTmVbUOJXlDScs7rqvl3gFnqmenPPMBc3DBnmss6mGmcechrpCqeH0ufIdl3vvOdBaz4kc8bDyBz29veth7a4wYjbdyy4w5A0zFoqcz7/OSZvJ2BGNY6Q8CntbcvD35HgJcOwtxGi45uectb1nzMyL24aJFDuONtb3tbHSZZs2yGDF2z2OWG8qk52/YAabYhAtQMI7SCFB+ry4FMh2FNABNgcw24KmPK78zyutdeexUoxttcR/7Rc+RwJie5hYljBbth1Oc973nFX96JwyprKAMYvsUtblF6mWugkJ+tk4TrNBNfhwxQ0q3FTYCmt1tymwdIz6lf+avj1atX1/SIBz/4wQU6AR5heC4l/7gss7ymi+hsMV/+5CaPA5GPvzbmjNIexm5+G4PGepUfvb/iFa+oeZce8qZTZG6gtgmA2nJKvRjaZcF8ylOeUtMsyIwfcLQx62C59KQ+UifKk+uUU74BfsL4a6uuTYP5whe/WC9fb3jDG2pbsVhZhCd+6lm7cC/aH/OJT3xi3eOGBs1rGwNM6ZB00uQ6fMbhFdh/ugamGkibc851taPWpK7YIp8JwGxMa+i6uZr1sS26KXA2AWjnnPPL4bvtmWER0TdO+MZwWjOCnNOGxoFHCzlvdCPGh+u0Z/l2NX3MfaS/PfW0HzWDyWSxKOSK90473bCMI3e4w17DjVt/mIWdyuKwO4a4eeZuSANYfP+43eZ11Rh1gLkh2ly/uFu0B1k91Sh6bKFYv+RXLFZAHACB0kGzDACXR7Z5YsBNAcQWbo7Y/e53v+EZz3hGPaTTONLhpQGF19hfmCN+8oo1AmizeIXVFLjVweqAdaghK7V1tiyZgBUSHpkDZpPGWaf9D//wD8O///u/19BiwpR3roEyCyoe85jHlPXy+gBg46mseOp4yEpmfrnBhLkGAo8//viyjLIymueZIWzhyPY/VrH+9m//dgFB+gYQHCj6Svz4WcFPFzaLtidniDyA5G/+5m8Oj3jEI4Z73OMeBShTduHhQU7u+EVP8vSdZy8QLKTKQS+OlNH5z/7sz2qIn3WXvqKH8BzLHPlmfU67A/TrJmkZyJcsykOf6in6HFu+xHHQjbhVfjppbmXYWPKTgXzydz+xaHu5YSV49KMfXW1vxx2v1sDlj4dPf/rT87sEqA/1a2qHlxMvWfjghzaG/JXRZvKj3OoXaY/0adHes5/znALurDfqNXUt3mIdsV4+61nPqhdkOrWS355/iSctwkfbS90lXJ3kXquI/adrYKoBbUU7cc4196wsmI1p6xMtbNtyuEqzLPp2+AQ5bNFGu04ZPvfZz9Yag/PPO7fJMbS5lNu13U1u1l5Ubzfc/W53H656tR1au17VpqN4Zm4znPnTM4dvfuvEGt078dsnNkvnuXXveNYClV7G7tSeUV6CJ/cFA8jkxUvvZkh+Q4leUO4nznldNf8OMEs9M/3Zoj20LhmDmlTCxniQ5WHsrWS8eOb1r399ASdWLiAtjUCnyKpiTuQ1m7VMc9FYqsG01jJemS5NOnLXOcZ+8gcSAE1nwPLzn/98zU9jafOWFZBhEYpGD1jZQBmwKMK7XYT/xHOyQTMA+G//9m/De9/73rpJyCmdIWTzIw0Ds2Cy2vEnD/nqcJe2A1/+KWeAmAnTP2/bBBm+NgT/H//xH7XJufylES9Dzs985jMLaKYseEUPkbd0OE1rK6J3v/vdtSXLeM8/gNWQqUULALeh/ElnC2g3fTe+rMSRlRzIA2FyNakvnTHrK6vue97zntK5NOLj4bvp97znPYeHPOQhJTf9kFd4eGqfy0X0DUzII3qSL3/nlE/+/FDkiXz071q9jnU7TlsJl+FHvupaXpHf1Art5O/+7u9q6JtuTcswzxVwVF4vcyzjWYwGVJoWcpcGjG7cFqj8OgNM1ZS6plf1y+JrtwsvkMC7/W3pSLhnijNSH9oHa44XJrtj3Oc+96n7Pm1KPHHETdvjHvNogfP3l/idugaiAe1GW3HONfcVBZiex8hCn0lbbdeVT7sHGnOLfxgLLAb9ftsf+eKLLxqu3kbPVs/dZFjTDBH6Ny+wCCj0bCff+e35dE7r27/2teOHY445pvoC95T89CnXa3MybV33iEc8vDoP2yQZat9qqzZFp/UF8zQRb955WRfyRQv31OTe5MamA8xSz0x/CmCqWI2nOkmV0BQ+frjNNMcRs9wM4we3yv6nf/qnmn/Jskg2xN/qaKBM5wh0IB0owgufNB5+1XBauuTjHD/nSqPszV95DZWzZPpqhxW2AGI6amDK0PyTn/zkArkZvtYwgSrx5K+DQTp0n5gzRG6+W/I1/G1fPF/6sA+ZIWUkbdJXPZTv2uXCQ5yQsgMFhiZe85rX1Fm+KGDSzQpUPOlJT6qh9/DGK+AjepHOtQ6TDtz4GfoTZpW3by2ziEbu6DE8xnzTrsqv5UdPiGw6Y3MzX/va1xYAN4wuvrh0aCrEve51r7IAm0qQjlv55ZVyFMMZ/8iDjPKgI/khYNtQvzlG5E3ZAQdDoNoIUMY/9YQHtwOFV9zlOeMfeZT8TXY6J4tdDLTDDxx66PDE1hbMz82HBMhCTvGUS71bKe0ly3e11bchc3WwMeSfsTpmwi7lDjO68lJqHmvmY3phCglL3asL7Yj+vHC4F81v1YGO21fqIGm5hSP5yzNtKf7Jr59/vTWgfWgbzrnmngXAnLS9ST/c2Bew3KK1zbPOas/DBioP/e9Dq6/8ZRv1u0p7/u26682He65ZM9z6VnvWS5W9Li9uAFWb3br1R+6HJm09m37SRsiOPfZL1VcyPLBkavcWDFlH8MhHPqKBzckIyoXtJdj9Y9ujBYvF+te7ciB6QZzzumruDjBLLTP9KYCp4j28vDlTvgfhxniATSrcjbEA0OT713/918O//Mu/zD980zAe+9jHDo9//OOHO93pTgVwyBo5xckDmIaELTSkyU0XPglz5pe0eBlq9jYGsHkzw5O/YUVzJMlmAQR/6fBwQwRMxWIFhLzuda8bPvKRj5RVCA9xmPwNtbNgGPJfLKN4jrF/yjUvr7xbGdUTnkDBZ9vijdc3YMhKNZYHL2CWzAAyYBg+zuPrtCyr9wG/8fQEYX/bVsLiZY/QvInKP+0l+sATaU8eJtz1YGiyKIs04kp3cFssccgUhAP4iMzC92kryt/cFqTstNNO9VYbf2Gul40afy8dKUfKZa6r6QiG9o866qgqizIAl89+9rNrfu5125s3UD9OO5YTL7Sc8ssj7dY1fZvywNrN/4HNEn+b1hbUD/kd5FVX6gD4f04b+j3iiCOqjWqv9sYUb2PIP9bX5nAdfUYW9xeiSzrL98pf+tKX1pQPepXGSAUCGLml48eKCeD/5V/+5VpzrcUVT33hrU7oHHHnvtF23E+dugaiAe1Ge3HONfdsAOaknWuD9fRq/fVWW20zfOekk2t+Z1aM//KX5wzXvfZ12hSqewxPeuITJl/4aWmASwaZeQNDAc7Gqcm35aqt6oVdv+Ul+MdtVABt256hN7nJXOvr7zjcdZ+7zn8MpT5MMOleJkUfX0981vlLL4heEOe8rpq7A8xSy0x/tmgPw6bjieLT6amAXM80txGz5BkvbivObHHAgmnV9ZiE//mf/3kNz87NzU068RZBo02DcQOguMsx+sFjcb6Jy9+1zsDQl61cxjLoHAw5m5f4x3/8x9VZhBddLb7W6QDJR7ahM1bR5GNFOouiQHW4owAAISNJREFU+ZcAa9Imf52IQ3x5pjMbFWP+UhzpWBltfv7iF7+4rCnmNQpz6ABZfA3ts5iysEmTjgsz8cb0V3/1V8OrX/3qAiBkQeS0ZREL7vZA6rTM6Qgji3jhVw+kqV7H+iGTMIslvtpWHR7arGp0ZUpC8sIHIAbcvMmas4Zv5AkoqgQz/iGraRt5IKYcrJeGj9/1rnfVIqiUiXUPKCdv5BzrYSwe+Rfrfxw+i2v8yexA2tCHP/zhAkIWYml7XnTIAqikjSUd/0MOOaRWSbNmuhcOOOCAajPiqofU8Szk3dx5RC8pd9ohf9esmIbKWf3NY7WYLe1U2cSLjtUJy7H5mJ4jRjNsCzVuL9ImfvjIZ3zPit+payAaSFt0zrU2MwuAKY/JEHl7dpXlcVWbj3luM8R8cXjrwYfUyI6XqlVtCPxm7bnCMPDgBz1w8gWxlsL0N+nJ5cW92u5UNu7TTju1Vp9bjHRyu3f0D3qknXbaedjrDrdvU9LuMdykTc9yT0zuk5S6ndfuukYBl76UP6IXxDmvq+buALPUMtOfApjhmIeWCkglJGzm51a7k+qecFbRLGash+ZgxnoolFweruYSeigbHs8DOHI6pwFdlqyJk3PKyu1wo+gwTMZngeQ3jvOiF72oFqB4I3NzCJ80+oWWzs9cNnEtaNHhpBzmvQEjOhjzOvHWiUijjG4ubjzHh/TiBDQUw/YjDpLmJS95yfCGNhfzlAaE8DWfxR5/AAWQae6q1emVl5tewsYzQIoTAfKGSPGQn/jy8QWTP/qjP6o40QmHcEflOZVnsV9ApTKmDN5GbchrOoJ9Oy0owiO8WHr4W1RkzmfC5DnubLlnSfJPx45v8gUwtUsA01zdyAkABwhbBKWseRi2SBgUD7yiz7RffstB8omeya89snS7d8ylNZxPfnoULq5zrg33vu997ytLvrIZOTAVQDsQJ+1uOWTf3HjSU+o6skW39CDMS6mpKp5d7nlzx2PJXFzX0rLKr2nDiIC7ZwHQOabkmXzxUFfcSB106hqIBtJO0m7m7+XWXK7IKvKag6mpTe8BgNXcS4sCP/OZo4e3v+OdE9DY2uPWW2813LGN5ty5jTDu3focTdR+7Gmr7S6quOUW2A68GEdOPPFb7eX9f4ZvtwWm5lsKtvJ89912LWv/zXe5eS2Iq3I2cbIXZweYaQGb53l+iFzFWdUIbGysziM3Q6mm5f+DNifPog+H/foS7sGqY9TRsaB52ApDzrmZFvsl3Dlxxn7xd0Y6BB0DC6Atet74xjcWWJC/PHUMAZjic8sz+pq/kZqfBUrimvc2BphWoBtm92WPubZwAkiVL14ZUsNTfgGervFmVbuggZcqZ4uDDFULRwCxPQ4NNQA55MKDtcrqe/qzDVIo+orc8fdpOwATkcuBFysj/ccNIFoFKy+86Ek84fHDm5XsgmYZ9oWj6JJ/eOuQARgPGnykFWZ/TfsHmvtpeyjlxH+xvJF7VufI74wyvA9gAl2+R/3ZtmoSkckWVl4aWDINkavPyKo8ZHa4Xm7ZS6jRjzKQh97lTbfOkcm1ONpJdCvOyW0fVMPqrO3q3BQLbVd85fh1G6JVZkeIHpBf7Rqpc/fYO97xjtruzE4UdFvtv9WBNEZBTEOgY/Hp104PXqDCX/yk9UxIW5JH2qS66tQ1EA1oO7k3c80NEF5hgAkYtnaszfkG+S/bHpbfakDwi184Zjj8iCObX5vS0Y7WgNtC2Ps3gHnHYZeb7dKGuScLHC9qC39YN7OP5QVtP003jr7DHeW5b3/k9733fTXaJx8fB7nqDlet56lnz57t+e+Wk9Y9Z7FPEcd6Uu6v3Ltu53ldNR7dgrmeityAaLVNURpmrDbcHmrLSSp2/LDktqjAAhPDeRbY6BTJZFjXvovmLNlaJfJq0AAadx6440aU65zFG9PYHVnMP7PC2fYsLFU6Z/F0DMCDLZKAoaTF0RCA9ABgth2x1Y9hZnNL3DyIHHNzc8MTnvCEGq42pIqsCE8nFZCGf4BWOhh+kbMSTn8iS4ZvWdmUA9GLzgvA1JmxQokfnUxZzJeHm6XYHFQ86D9xfWHHPn6rV69esn2kzSR+ZB1be5OfOMLJZ3jxb/7mb2qYxMKuyAdgWm1v9b6hxPAVnvoOv1mfyZayqxNklwEAMxZM4eRQj3S21153aMOdNyw5U2dkjtx4iJ/64l5OUgZ5yy91IX/XOQKeU5bo3ouWtvuHbccA39O2SbjdE8xFlmZjlWE59bO+vFOHOdNd2sQYsNOJ+WjuP6MJAYnRP90HbOKhjdzqVreqRYO25Upd+Fze+LmmblD4uBa3U9dANJD7PG1UW6n2MgOAqa1eUNsLtTbX5l8ychx11NHDZ5oR6HOf+3x7pjWDz/SZ8tSn/smwT7Nebt+2MmoPnxIv1kZzMes509p9jZo1Gc9vq9D/7/9Or3mYAOZ3Tjpp2K7Nv9Tvbv8b29con638GBiUjUV1UrZpydfu0qOOJc/SI+kRZ/TGpwPMUstMf7ZoQzqXsA4artVRAlIqoLYCmFbETHNszFSqRosmjWVS4ax+LIeG5yyo8ADWIM0bW9OGk1izrCRfaCALQ1ce3kVazUjuNKqcJ5GSL9cEbJEHXxutZx9OCznkLy0ZWKpYcx7U5pe4qSp1SyPvgEEdDz7fa6vr/uu//qu2+7FpeSh8gEyWOUPW4svHIX2AGpnknbK5dqT8OUcO0wvMAbMnmS0gpGMBWd0AIevaOL/wwSPXkRG4Zr0FjM1JFcfhG9VWFJvPCWhIl7ydwydyJbwiLfGjvMrKwsvySmesx/K0GMm3mw2PA5dWlaPkEZ0swXYmXvJRp85kVKZYMDMHU/2QwxA5C6azIXLEfy09ND60xS9HRVymH3LTLyJ/7jfXaaspW3TJjZz5sb5r7yyX6t4OAp4Vv24AsymkAB99RjfRk3Pqk4613S+2TdhtT+Z+tHH9OG7qAh/ptHPbr9Gze9TQuThpP+LkOVCMpvmJ06lrIBpIe3LOdbWddktfUQum9heAuUXr9wxrH3roB4aj2hD5CSd8s7VjIzRte6H20vO0Nvp1pzvu5SFSorWgAoUcXppsYwRcBmACq6ef/n9lufRixsjEemnE6+o7Xr0ZR27W+pzfKQMTK+lWbRi+AOuEve57vYleEL0gznldNXcHmKWWmf60et7yktUNgFjVrJPUQVL6cj7A8NfJeYjKJxVvlas5d4Am61k6eIthrCC3SAXIk2ahkUw6Q+7wSVg0xT8Hv4TnLMxN5DBXzUIec+xsoxO+Hvy2bNHJ2l4k2xQlD52P9MqEzmiWuM+3Dsawte2KhEVuce2FBzCz0gH10i01tBoZWTlrKKLx4edIHbmW/uQ2rElmefEDBABYiwgAW/mKJyyLWOq6ld8Z0UWGgcnNCiNMXsAFK9Y///M/1wuJ1dIAMZ7yTJ3GHflSj/gIiwyRx+Ker371q/PWMUMm2iG5besUufAjHz6RtwJn/COP1Kdr+ZLZS4+XD0PkXhoSBiD4ypP5tSzE0f9iOcUXhqKbGYte7CqfaXtsiqo6Gusr9aHutAOPXfJof8otvTBA6ZnNYm8ahm/Em5urPUUfyyH75swTeKQb7d41yo4B9JYtvbRnoyCsmKzA6twhbe4N15W+6fa6bQEbCw39rmkv0ij3Se4VfniM3fw6dQ3QgPbkHnfONfcshsi9Gtf0uXrutqk+7XzQWw9u09g+N5zW5h7bdJ3V3d7UT3rSE9v2RLeq58o2BQZb/PY3mcupj2l9f2vHkKF9Md0F5oYff/zXa0qO3VcuafyU4YY3vMFw+9vdvj5uYt440Gmbo1V9m6KmtZVBBTCBHF+oMYRqz0GVq4MZd0qzLk4euPJwADLmtVlEoiP3wM7wtM1aDY+bt2g+4bpkw7P4NWENOedhzC83nnIkz5QpYYDtYa1DeGX7Bno+GZnOWL5PavvXsQKyqOEtv5QjeZEb6YDs6wWMsc4pi3yQ/N0wtrcx/wpoBqITJny+LO2aDCH5jElclA5Ovq7Fi0ymGEyG38Sd6DuySBservkD+lZ2W9SjsxTuACzM4VR+Q6b3avtUKof6WEz4JP/oSJzwch05geefNcslEi6tCeO++JB5qQnLWbzlIvk7UK6VhYUV+DbHDsCMrCxPXkroxTSKgMekTZkTH9/llH8sdyvAdBXnwktU6mVBPvIstCv+5GPBtoG/EQXWZC9LWYySMsrryk70oa2mzqIf5XaNxmHudQsFDzrooHoh0VZy/4tP/w7k3tDGvQCa/uNDEhblpY7wTR6VYPqT9GO/fv3rq4G0SedcazuzAJgTrboH9EOsj6uGV7/2dW2Y/DOtf/hZTQu7Wlukdv3rX294/GMfN9xqz1u6MSo+GSb9Q3v+LBoib0+ZAphGsIyWfPGLXxhO/eGpdU/YB/OmN11dff6d73TnZmy4fvN37+iLmhiT267deOtf57mPFu7Vyf3LjU23YK6/Ltc3ZgFMc/RYMAFMHaSK0IGkItaX2QbFa3kwmSP5WUGskZmLZEWmvAErZ5YhWxf5pjYAPD//qKXDoRpIi4fP+FifMug4bKDN+nf44YeXxeFTTY7zGlDzENcx6ACAB/MvgULTCSK3DqJlOjH7j2Tgj7cO2vYlLKMm95OPvM7KAqSZmmAY0jxTVjuWQuHJI9fSKRO3A/909OR0I5NZ3uKiXHOnU5I2MiSPcfyfNAviUQ1UAE2GLDKfM+lYbdY0S4tPCZJ79erVww1ZG1snGXnEDXl7VddJX/7c7SL5CiOrs/rNNZnDK/JLn3TFaz1/8ERjPtxjf3klP2Eh+WWInAWThTsEYNrKRxuxTRH+4uOTOlqcp7Qp47hOU66ck8evck45Igu3PMfyjfmK54g+zCc01Gs/TC8Spk0on/thqfKMeV2Zrsd6U67oMNfO9EYn4uawxZP55G9+85uH05uVxrSV6DjnvDxq8yzh5hsb2fAMiJ61ITylGedVjv7TNdA0kPaRtpf2NROAOQVxrJitFbZ22HYUednLa4j8l61P22qrrevrPfZHfsxjHj3c6pZ7VJ2cf571C+153voL2xQBiM5bMkjUc6ZZMxtoPe64r9WLu+fr2W3kUn+xqu2PaX7yQ9vuJ7Yo8mLLaFT7YHo3S/cylW19GgHdoNxHnNEbNh1glnpm+rNFAwuXsGDaSBmAyhD5xupAVDBwxGqpI7M9EICJ0iDMbbNgBgAz9w/A8ZDPg1cHHSuaB7YwfulIuRca1aTTl1ZcllPg0nCwTbRtNZL00oinU/XpwgwNBxCkJsQrWVtZLPgZy8V6wRqoc86XicR1SOdgvQDUfKXIpxiV00azLbDC5SceSl5kZK1UbuEBmJFtcR5JN5bZtXjkDX/XeFik9LKXvay2XLGPJ10t5klu4N/8PEPnthVK/eBBRmlcj3XCvwVMVhG2c/hGttRb5BOOxm0y8lbAev6kDGkrkuGTNjMGtvzJ6Ux+Z4t8fPaTBTPbFJGJBRrAvK0XoPaCJl3KoNyuM0SUsuCn/rjTnl0nXYok3sYkMqTezOc1bMvCoH3aFcE+rtdqWxzVHKqNKdgKyosO095NM3jhC19YX0fylS1tLO1QXSeeenbvGKWx4M2ojdECcdSHA+UlcgWpo4u6ETSgzWlDzrnmniXAbIxbSVqf2E4veclLh6PbiKNm6VnAgrlzm0b26Ec/qq343qMsghecf0GVfOu2mtxUHJ7AaZOw8OFFjd9ZZ/28Ri7f+taD2hSTNt+/pdimRq62qaloT3rik+rZqf3r49w/VqTP0wY8HukF5ZnKOa+r5t8BZqlnpj9btD3YLjH8ZS82q7Wy150H23JSKjtnHbZJvvaR8zWdgBKdry/38M9wrDDpckTONBzu8XXCdejm9+Fv3gfgdFzb6NtQIIBrWNxenPhqyHSgUVvYYvgKCDdfUnhAR/LRUbgJdADSuGaBwM8qaZZMUwAMs/LPULby4YEv6+Xc3FzNx7K4RafOWpq8lEPeSJpcL3aTO6AJf0Qefkt1UPikHK4dP2tD4ye0zeLf3KwvwLfhfjzwphtlRN4sAXBtCNj0MsDSrIOUF17SOIdc01fkWVw+4ZFfmtRFeJAVz/Ul6ciOSm900d6ciaQukDieVbG08otOyCo/dWcOJoAJONAtPbBc2p4G0FSHofAbg4jKp8kf3s7KLw6Krubrq4V5admQ8ib/DT0rC/nI4yXPV6iAHW1Y3dqA31xMdb0x5NlQ+Ten+PSI6FFb8WWsI9tHFzKKIUwc94g2oL6dLWbzsmb3BM89bX98z1Z/Omo/+HTqGshzxTnX1a5aM7yii3zqwUjF1aYvH2De8hbNglnNf/JM3aINa0triJwV84L27AVKWT4/3kYNTcFh2KlnCvnbvbBPM7Tc7a53a1/x2bs9Gz279VGTfg0790VR3RCTy8v7pRdEL4hzXlfN3QFmqWWmP1u0FcOX2HRZpwEYeItWAcvbgejw165sHbfvdn+kDSn9YrqwxENXJ27+o0U+LGYoDSSaEG/SUIQtAI/F8XSgOktD4s4sUkCmhTEBPPgou7Q77rhjWeXsw2XoyvSB6MU5Vily4B15gSP8dAwIqGUNsq+gleksGbaBSRzpAjAMjZmPadjV1AVlB9b4KT/50uG4DrkpM+XAzYcfvimLuHGnDEmvrG65cOMvvUULhx12WA3zsS7rLFl85S88MuOn3QDDQDErF6u4Y25urkBXdEFecjgCMKWPLMLJg3dApjYgvTQlK3nbsb6U8ozzwAvRVfGin6k+uRPXWVwyegkJwLRCOHpw3wCY6swIQHjjI13KAbjhx3+cBzniF3886EAaB//lJHLJz1l+n/jEJ8pa+6b2RS3fGL5pq0u7B/iGtikdytVp3RqgR4cXSS+0vlfuBdr+vtGzOqVrpL7Ft6rccwbA9DlJw4Tje0caul/u9rDukvWQzVED2o42kXbn2rFJLJgjgDnR1UQ2z1dt/7zzzxvOakPhP/rx6cNh7SMbpo8Z3bN63CLC67QN1u9zn3u3xUK+ctWep9NFP61AtdjIPMz5e2IDHot0g3LvcEZv2HSAWeqZ6c8W7QHYFoVd+qGVSphpblNmk5tAB7/Q0VpAwMrnE4vAV26U8cM0fthEPuf4Oy8O4+YfPh7k0jjGD/oAGPEBGvEBPIuODI+7lpa/Y0z460iEs4iFN9CpA8FbGvOx3v3ud5dFEMANEI08S/EHLHXsZADeyAbMyYf8udGAwZRTmPzwI5drcsRPPHkKR8L45eBOuDw8AAyXs8Ce3KYTKBdeDoSPtM4Am0VFLLI6SIdhf5ZOYY7kI2144Inkq0zO4pGfPJG/Im3gT2QbvwSQlVtb83DRcviRJ1t0BRim/gBMLwmGyVml6IZcAJfN81n5jACkLMLU12Tu0qVXjuMrDqIXbrIWOTcd0INjOSn6UXbXymWrKpZa1n1WNy+hLLQ24bcgsNO6NRB9pi7Vn1Xl9qk1zQDgTNtJXG1PW0hdW+hj1wrfgecnHGkjjsRbtxQ95NdJA9qRNuGc62o37TGysS2YGSK/qG2KThYPWI+weuY24wsD0re+deLwyfaBjW+3VeO2ptOmt2lD6avnVtcoyW0smLzOdasKPf3Mv7RVkuHzPkS+clp2fSoyHQuQEhCw3EXQ8DxknT1YzW/09RjWPn5k0iAd1UibQOOHavzGckojjrMGq6MHEsKvbrjpgzpxyCBO0pHFYV6hFZ1r1qypIWCWBXwSLw98bkfAInnSAaR8SeMtDUAz+d+wM2uG1abihV/KI438ADVbJNmux/CZTt42SbY14UdWFH0448W6unienLDoKPnwc8gPkUUcBwIuPABMHziyDfGxZBrSILcyO8bx5Y0XkMsS4wBOLI7SaeazeOJobwCYN9dYD6XHkw6VLeUhC7/IG/n4Xx6x7triSZ4OaQFEi8os7CJH/PFH3KmTyAGMmlqhHlnBIxvAz9Jszpz6orPUPZnxERfP8JeHFwdgbbxojD6QNMB1gGd5LtMPmSKfejUVABBicWOxJov6VD6L7ci9IfpfJrE3a7bjeiaoe8gw4MEHH1zWYYvnxNG20i7S3sQ3WnGvtgDQ1nFeMll2hOdek6ZT10A0kPvXOdf1DNsEAPMWbQTLS7UXdd1IE2n48ek/qhGwU1s/YjP1U0757vD9tkesxbR5tuyxx+5txHCP9nnI3YYd20ieZ+eFrQ83lK7dK1f1N63pey4WbcBtID3KvcM5r6vm3y2YpZ6Z/tSnIqshtofXxgSYWp23Eg1Gh2zFsq/H6FRRGoGHahpBGshYA4mXONwOD25pHdzC4xamwfIfAwFzPFevXl1zPVkMATmgLgAjfCJf5Enjx8shvgPJE4mDlNWQvKFyw6wZprchs7DEr8ijH3kZsmclAy4dhs8MQ2foPnmkTO49exim7OR3RF7+jnG5msc82BNP/YjjIWAbJ5+hBM7I7WBx1m6QMidvaZOfMPIaSia/OYsWMrH2AVSJ66z8+OEVkEkG/tFpyoDv+hD58RjLY39CnyS1IhxwRNFFrsuz/UQ/whE5HUh5ySaOskQHXm74i5d0laD9RH5zGk2/oAskXvhyj9uSNMtFAeBk1w5Z2m2txWIdgEwuD3ULVkxXAXiWU6blKuvG4Kse1V3qMvo1xQTIfPOb31yL54xi0Dk9Jg35onPPH4vH3DvAfdqR+F33G6MmV04e2kbaUa65N8UQ+S0BzLahOiPHqaedWnsc2yXmzDPPGM5obd4UsZ82K76v+Oxy811af3uT9pJ9gzZNb26wEv06ra0jz09WUPdR2nv4ukeKNuCxOL5/pPU4n9dVc3eAWRqd6U99KhJHis5DMR35THNaBzOdu6FHHZdOrW6K1rDGgEJSjawaXHtwI/HIyZ/s47A0JI1Qpyge4Jqhy+wLCXRIDzwZwgUqfVLRmQVB2ujFGb+AHWHyjJyRI3lwi6/9T2DJRMf4iEN+e0yyZlpl7nvcOiD6CDipgrafcb0AMcqiwyErK2usgsqVG08csvGT3nXJ0/JF5BgP3QpPGeWvnPQmjbIkjTBWLkCTNdNwMesmS5d8Es/1wkNhMpcWP3nYQ9NcPosZWIaTz1h28aRXN2TDj5+4jvCuDC/nR7pK3/jEUspyaT7lO9/5ztK5PJQt5XWdcqcs3PLlFh9xO+QRNx6Jk3JEXjwc2o8V2o961KNq+oV6BUS80YurjGlj+EaWymTGP2QNqLcH6gte8IJ6+cmqZ2VI+3jOc55T8wO9eC2nTDMu4kZlF32qP3XpXkJpx694xStqqowFhqHESb1zW+RjVw0vZhbSpQ3lnLT93DWQ54xzrrWTTQEw9zQHs5H9jY0SvezlL6t+0Mbq9ez2DGzPyG233W54YOu/bn/727W5ljcsQFrtf6tmqGiCtx2O2gLHdv9c0J63nrmtbPUSb5VPaHQZr3Wd6QXl/uGc11Xz7wCz1DPTny3ag6zpeNIo5zuM5rZyNRUx0xynzFKxho6y355P8MW/bo5pJ578+eUQL5288KRzDunow0fZNF5+LH6GnVksLW5yzM3N1UOcVS170IkffuN88ccvMuhQxE18/kkXGYWF8EJAoCkBDiCbHqzcZdmMVU0+yasxLbCKN+BlH03Dq4bNH9ssYbs0S5hhc/JEXmmBJXJIk7zHMkYuZ6JRYeR3dnMbYqY7XzBieWZ9MVwMhADJhvstDAE+pYnMyYdb3mTTWRpSZoE1R9PQOUCfNOJJ5xw/8ju4x7ocy76u68iQM74WL5kPO7Zg6tQjZ/IS17W0iNuBImP8lA3RE+J2kBffhAuTxnzGx7TdCW7eFnF5EaiytrxaoCjzaeQTnhUw4x/8lZH1kl6MJnjZiWXaynj5a6O+QGW3CfNOl1OmGRdxo7IrfbZ6zxQVuk2bce1eydZlploEXKbtRVj3BYDJ4u+ZhaRHG3oPVKL+c6XVQJ5FzrnW5jYFwLSKvL7G03qrL7W+wRZE57f5k/oGH9SALVatan1JewG7Rps+5fmyevXccNPVN62+eOedbtjauU5o8pxUadp9nqWrGsDM/VTIVYT1IHpBScs5r6vm3wFmqWemPzVEPn5ouaZ0D7BUxExzbMzwlw/+ZzbT+afaCus3vOENtQ8lPw9aBxnM3bMqWZrxAzhyRjYPaaDMA5s53t59wKtOMg3TsB4wyTKggwQyNW4rfx06TPksBhqRSdhSOhE/cciY8pHNNVKWyBweiSeMBZDMhiVZkVgFdeg6feWRh/jKMtYDKwmg7BvttlGyTRDg6U1PPuLLF4mL8BHm7MCv4rTrFlBxhDv44+GQPuVIWaS3Z6ZPPQKahs3N4XOmfyB6nGd4kg/QXLNmTc1DdPapsW2mIFg8JJ+s1nddcjX/dN4V6XJ+yJi0ouLBYmzfU8Pk2kh0kHKJx095nZU/1lvxUx/i0YvpC9qPuA55yFc84dzjupDuwAMPLEuuaQ5JKx6SVnxuBzmWi+TlyLQBK56VFTn7CIPyCWcx97lULwbqsNPSGqDPtCX1p03w0x7M4TXNxAcYbM/mPk+7STrtYU27J1iTV7dpO/SPhCPxO3UNRAPahXbmnGvuTQYwW8YXt+2I9F9f+cqX6xn7c31zGybX/s8486fDmT/7aXu+XFjz1r1AMZbcrBke9Pf21Nxh+zbvuG1ttNWWk2cfw4an4+R+mjwnO8BMC9g8z1u0xohKumqQrRK5Jyu1ppU4Y9nx96D1kGQpMVTJkmQrn4AgZ9Y4b/AH/sEfrDXMPH5w63jxO7eBS99F1aCPP/74WrFp9TOgpqOWn2Flw98HHHDAcK82gd7ijFAe2HhVqac3q3QBGfMgpcVhrpcmN7U8pNUxhMIzbrwc+CDhARbcOnNub3pWnRqCdgBu+Ta79CH5BbxI67OT9utc0zomYFo4gIenMozr13VkxgNf7uhT+Lgs4zzxFG+sF+HSGDo3/GyByMltQRNwLD7ewpNXyqEOWGDttwj0A52RA0/xkhe3MMRvfYkepBvXl+FJm+Af2RYukU+YI7LGLR+WX+kBZl+bMIfW9AaEt87fQ5EVNlMSpEfqWpm58Ujb5X5I+0rFmlZXWeQjjH/q5vwGZAFu6ZeT5Kvc//Iv/zIccsghtXVX5KB/c0W9jAkHhr3MPPnJT66OYTnlWsm8tQv3pLNFbK1Sq/69nGhPRi20P6v1vexoZ+pdXdC5tmRrNDrPDgzCHZ26BhZrQDvTNpxzXe2pde0bfRX5dJsibV1z3Xa79vxsbfrc5v7FOb8oA8pX29d7vvb145tR6Nxq72S98MILaosi8/T3bS+1c3NzrS+e9Bmsljrmiy+CVSZ9b+mg+a0v0QvKPcQ5r6vm3y2YpZ6Z/vx/AAAA//+90KqlAABAAElEQVTt3QeAJUW5NuDeXaKCKCpiZFdyUkSQIHrXAIoEuYpZFCMqhmv6zVfECBcBs1e9ilkUVJAgKpjJgooKJhRRQQUVFAEJ+9dTZ94zvUPYGTizOwNVuz3dXeGrr96q7nrPV6HnXHvttYuK6+JyPW/evHhNy1k+jl//+tfde97znu7b3/529/Of/7y7+uqruzlz5nR3uMMduvvc5z7d8573vG633Xbr+vpER4qJ676Uo7vyyivrcemll3a/+93vumOPPbb7xje+0Z1zzjndFVdc0S2//PLd7W9/+26TTTbpFi5c2D3taU/r7nynO3XzlluuyiGLu4YOc+d2c8sRJw/hibOo5FeQq8HRLXF4RsecyXJd05W0Vb78ynHNNdfUsMi56qqrussvv7z761//2v3hD3/ozj777O7EE0/sTj/99O5Xv/pVxSh69c/3uMc9uoWlXK9+9au7tdZaq1t55ZUrLgO9xRxgVa9KvjDj5Os69ymnc/R3Ldy9a65/5k/vf/3rX93f//737sILL+x++tOfdqeddlp3/PHHdxdddFEtk7JGrmt53+Uud+m22Wab7rnPfW738Ic/fDHZ/fxrwNif5N33u6FrMuie+tTGLrvssu6SSy6pGNODS9nEi478xF+utJE//elP3be+9c3uq189rpaNn7Trr79+94IXvKDbYIMNutVXX73GVy4ykm/0Jc9R/nR3XmON7o53vGOtJ37ikkeu+K6d6RPdb6iMN8f/H//4R/fb3/62O/DAA7sjjzyy8/zQx7Oy3nrrdS95yUtqHQm/+OKLu80337x7+9vf3q2yyio3J9tbblp1WQ4u9aoe0wbU85///OfuRz/6UffOd76z++Y3v1nD+nGe+tSndrvvvnv3sIc9rL63+mHkRpbr5hoC2pk24Zxr99eWZnjCCd+s/ccZZ5wxBErYzjvv3G277bblvbVep7V6qw/e7IN2Oy5nLBnZJQaZBxzwzu6kk08u76ySpryfbrfqqt097n737slPflK3YXkPXvXvf3fLLzdv7L21qLzLrq7p5syd03nfXFz6tgu9T7/9ne6Xv/xl7TP0zyuUY6UVV6p9/5ZbbtltW/qFvAeXX35eKduYjlF0WKIlXygPl2fH7bCMxf/qaxYVrE7oTjrxpO6HP/xhjevPcqUcO+28U+mjti7vw3V76cf7wmHkdrEYAnM+8IEPLLrd7W7XISc6jtvc5jYVwFTCYrFHfKPh/PjHP66E6KyzzqqdVyWYJZ+73u1u9QF4/OMf3z3oQQ+qRIROaSQ3pIp25+WOUJ566qmFDHy1+/znP1/JAQLq5a7jRF6f9axnVWKzYMGCwYM1Jl8ceaVjdz9siMWfc58jhIS/dInP3zXXJwiRVQPKHzjwQywiM2VVjr/85S/dT37yk+4HP/hBJWwI8x//+Mfu3+Uh7suXx0YbbdS9/OUv7x74wAdWkimcf/Inf6KbqHPiJE3iJy/kqa8nSIhVd/NKXl444iLHP/vZz7rvf//7lST/4he/qGRfmDxrXZfziiuuWInWf/3Xf3VPeMITurve9a5DLJInHegj3U1xyVPaXAfjlJe/sqXc/PnVchX/3//+95WAffazn+1OOeWUITG/3/3u1735zW/unJHlifLlU7Ue0z3YpRzyi5+2EB3iJ150Spolnf2QKWDVaPQJdlVmudcKUo9+jB1zzDGdcp1cOo20x7XXXruSHETHe+ETn/hEIdjf6u5eOpIPfvCD3e1XW62bO4YXuVzqx33wrY2jV2/9ODXeIGFN35cx9JiFF8HDOdcpxj//+c/6PHzlK1+p7cmzzakPP1Dg7of19ttv3935zneu/sEsWA7vI7Sdb9UI1OeoPGNpb9qHY1kRzKuLsQFZnFsI5aJFY8aL8jqaN6/0ceXfv0v4ZcUY8YMzzuzO+MEZHfJbbF2ViDJUrFHa/WabbdbtsMMO3Z3udMdu1VVW7ZZfYbnS0ajmELvBO+f6K/66/USewzw7XlnBTexGMK8fyZvjW7Ces2jdddetL7PXvOY13d0KsUsF3BzBk0mLIOmon/GMZ1QLSjpC5/nz53fPf/7zu0c84hHdxhtvXF+yk5GZOBoOK+CZZ57ZveUtb6m/SFgN0qC8yB/wgAdUkumXnF9P8hXe7/C99Dn+wmETfHLWIcffucopcVlGpeMSd3hd/PuPR/RKPPfkkhWLFuvRSSed1H3hC1+oVg/lQX7EpbO4a665Zq3LPfbYo3vwgx9cwz3o0SX6iCtNyho5cAjBkO/cUp6ifC3DVP6Qryzksoip5+OOO67q/re//a36ixM86PXkJz+5e9KTntQ95CEPGVr1pE8cdRF8quc0/wlW8nR93nnndUjBZz7zmUrEkr0fZvvuu28lmMhx4kfvxIu8if4JH9VZPupQ3XIwhF2/rvmnrlnGWSS95P1wUS/Cttpqq26fffapP1o8q98u5PIj//d/tT1+5CMfqeRnpZVWGj47ZCYP1xxd+uXml2dKPnl20s5qSyt4L816ptN0OGXj+uXVefqh8vWvf71i7sejH5Ew8kPL6IofWTvttFO1jJPRr7vgkvN06N1kzj4EtJ+8d3LtfqQEs/RYJZfyfrhxC+bGG23IylP1KSqU/qOHZ9Hz2kI4rynhDEFI5Q+LJf/Qzx1aRrkuriNg116r37um/pDdYostaj82f/5a5b0zGGlDMBkyluz6GQ/eRdLk2SnZ1+fOvZiNYC4Z0anGmFNeaovufe97107d0CqCqZNIJUxV4GTiewB0LojH9773vW7vvfeuL908GGQYmvt//+//df/xH//R0e+m6KNTRMJY/j784Q/XIXMva3k7r1YsMM9+9rO7pzzlKd2CBQvqC14+fT3619JFD9fBSRzOuR9f3P5RI13Pn6RPkHsdER2lRxDkJU/DuqyChtU+9KEP1XvxldVZJ4U8IzyIe192dNaZ09i98OjoOnnJm0sZ680U/iRfHSSib0gaQfvOd77THX744bVOkBN6K6t8WJV1rC996UurlZleRcHyMhq3AkfXKahyk6OmDPJ0PdsIZsWqlF5dpyzq1bW6V9cXl6kLJ5S29N///d91GoP6UGeslCxobyz+hvINa/mxdvBBB3V/LT8Q3vCGN3T3KxaGNccIddpS2os8yHHvcK869TbJf+A3GCmIrqnrtD8pZptL+WDC5TmO/8c+9rH6DHj3afv8Hfe85z0r5kYg/FC57W1vOwyLHPE4mDbXEAgC2kXeU7l2P0qCiV56fjXrGxsirwSzRC3Gy4HLeVzZAbks95rz+ef/vozMnV6G8U8qPOD8sffGYGRrtdVuV6ZOPae772b3HUQusoirP0irPhHaPyfDnAdheXbgwsl7iFW5bwSzwjLSP4sRTBZMFjAVMJ0veJXq5fu7QjiOL3MedFbmt3F5SDbddNPuoNKZIR3mY05Vn+TBOsBi9r73va/79Kc/XYebQ8bkZfh91113rdYz8+G8uOmWFzjz/pw5A8KdxkhP1znrqBEohNn8Q52xPMyBXFCIa7VqiTzWsGvCCX8ij7frdM50nEj65GGOiGFK5Pn888+vccRNeoTBMJsyBdMiuZZF2US97LJ/dYbrYIS4spCaO7nOOuvUYTpTJ4JDzjWDyfwpZYAQLOmv/hDN3/zmN5XsswSaY2ragrIqM2Jsztm73/3uqrdsxnVfvFNNWSejyk2NkzqJDrOJYIbc0N0RjNWDe+HaqOkL5l3+X7FMuk89mwv7n//5n9Wi7IeAubWe0de97nX1B84zn/nM7pGPfGS1svXzCtbBbtDWBqQyONKF64clnbN40aPvP1uulR0mzsoSvD232v973/veasH0vImjrCzG3kMwf/SjH13TKK8wcfqyInO24NH0nH4E0j4mtpVlQTA3YcEsbjF6t9iNnmj8+Oc/LisjJxd0hx12eJ0KduWVVwxDzdnca6+9uq23ekC3SpnnWR6nRjCBO0vcnEKCFpnz89CHPrRDMM350UjTEU1HOcj3AmYROfrooyv587L14uScTfA1J4wlRVwv4IRPRqd0ejozJOZLX/pSHZ41hwwZ5JTRC/z+979/V+aiVkvpCiusUMP6eTHHm2s20cmDbLob9kJAdCAOJNO8Vp2GidR9ef3rvky4cM6JEz/38VMmnT1yYKjScJuw1JlwllmLmMxjUSZyDHmXi/pwi09fh2E6i4csJjIX1pw7Hd06pV0o9zBtX9lJXPcXQqVcMDMn9tBDD60Tqllw6JKzYf1Pf+pT1WomjbAcfrVm8cTSICB97F3PFoKZqhnoP/hRgeS777cRbdcPr8997nO13sXRRvwwYkV7YhmqvXdpA8HBVIvnlWkrFs4hlxaiGMJSd55PssUlh+PH8VOHzuo/+YjPuddmxZF//GvgLPyjnCkz9ZXLjysjD4cddlidZmFBo+cSqTfqYO6uUQfk0nzXPobSww1GsFkabX8Wwn6rVjntrd/2arspXcqoFvmM9RylLd64BXOTjTfs5gy6srE6cTPGDFNL5TZRyuNS2vui0pf9X5lKdXI10lgUJD/vg93KM7HNtlt36xbDBz/9QLNgBsiZfZ5TyNUiJM58K6t4Wbw4jXO6nIfAC/NbhewhfoikX/f8uTXKkJyh8QMOOGA4J3SqL9XkkQfOanUd41vf+tY6bJ6yeWGz2Jnvae6flcDySunrozGGBZ1rWLmPrueV1beHfPzjdf4aS6LOFoFljdtll13qkK/FH339q+wxmRNxjr6RL3zitXsd1gUXXFDncbHM6pDETfzHPOYx3ROf+MRKBBAGaaJD5B188MGVdEtLZ6SYFZbl0xxOPzxC7G9Kpw8LmFViWwCXr3tW16OOOqqSevlFH2eExWplUyQsxpI2GEmbMsYv9Tgd5+gVTGcLwaR3dIeL9paXeb0v7UR9az9ve9vbui9+8YvDH12rFivBggULule84hW1/VopjtiQpy2Yz/z1r32tW6MQIlMwPKeIkvYBJ3UUJ03qS9tzL9zR1y/PVK3TEqckGrbVyJqNZ+1fObVhc1u9f/bbb7/64zA/cj173jnIuukhFunBMpj3nzuygtVsxKPpPH0IaBt5T+W6Po/lcVomBFNRhy+d4cUAgPJCKtrW60WuS/A1hWCyYJ544vfrzjLmW3rfrLjiCt0WW25R+MkDihVzq1rG8iopqSe+1QaiB2+767uWz0CP+p4pUdwOsSr3bYg8uI3uPKeQjEU6FfN/WNr8mtYBhFiMLqvFJckDubRgwosXueG8QJELq8de9apX1V/2GkQaxeJSbvhOw8lLWiyE7MyyiOF1r399tdQhtHTw8me1ZTHVYbLM8KMHJ980TPH7L3jyrYy2zRKybLsF4eKZaoBgInlWdEdeFTr2p5ZrkEnfu+ZHRvJ1Tr7XsvSM5aFMhiz/93//d2jxiCCWUyvwYxEhg0x5kuWexZrl1hB5nDh77rlnnTKA7LGmiH99+ifNxDPZyc9ZJ0mugxw4fbVsIfXWQm5MXxAnON/3vvetK7KdWXXSDmMNjf4T85yOe3px0W02EczUNf2Du3K45qzw99wdcsgh1RLOTz3Nnz+/Eh2LrdQ/FxzUnZXkRh3UoTnSLN0IJrniiZP40sozR/KOHu4dnjcHFzl9YlUDZtkfGCCPeQf5QeV9Z/6xMsJAGLyNHvlRZ665eeH8g1m/2MF3Ks9iP327vuUioG1oM865rs9ZeYXNOIJZqmERXceqo6hcnpVryhqJr9btlGxvt/zyg5GMefPmdpveZ9O6RdBDFi4cey7Ke6WmjYTU68B3cNe/HvjAhYML53aIVblvBLPCMtI/cwphWWTYUQdupTHS4sXIcjHdLzILb8z9slWR4ToVL09kz8ruvYpF9Q7FEqgRTFWXdF5e1unEflusje9///trx2q4ilwdGRLjQLgMLa9SJtdnb0z5pmFGjzRQOp977rnVCmibF0P+sBOP9c080he/+MUda2LSqL3Ic82/H8aPi/7OHD3FUx46uWYheVXp5N9XysQ/coXFgrnjjjvWxQLCDcctrzMvZeVYcy0UYl3hgrGV+4gDkqrDS+dfI03iDz3gQOfgF2smMoKcwIs1DMHk6CzdZmUy9z77vKlumYWkk5FyOYuXYxKq3KwoyTe6zTaC2cdeHSpHngcrxlnTtFn1z99cy6233rr+aNF271T2iOWftgdMi8uQpI+VhSrmSD+rbPUlH20LXn6g5hlwzS8HWZxnjdO2tQv38oifc9pi9Zxlf1JeZ+9TU2jMM0cw3fPnlNF0FIcfo3CwB+8V5b0CO5gEs0CQbcDUZXMNgSCgTeU9lWv3y2QOZhkixx7HW+igvUdXAXw8BkXr+jxcVQjmCcefUHcbMU1rbpl76f1w9dVXdZvff/OyVmK77mHlh9jAScX15Y7n1s95EH/wN89dnp2a/xhuUjeC2UdrNNdzygtsWEt5mamAfqcymqwWl6Ky9ylboLD+6ZwcOiZ5P+pRjxpa31ZddZXaEKfS4ZDtCDkj073Nvm2Xo3O0P6ZwhzDyrSZ36GR1tvzh4MylE0wDZfmzhyBZtg4yJ7KmKbJWKsPSphtYbGNBRNKMo0DH8TtX/TjRnX//mp7ygJVhtte+9rV10Qw/YTmeXCxQTyrb/my33XZDq7QFS3PnDkgfmayXHy/D+x5o+Cet6RI6vOc85zmdhT70EjZZRxfya3nKNSKe/RL52/cPwbTJtCHyvoO9HwGG51lPERB5w95ZevKnSnr7eUz2Wj6ccrieLQSTzvSN3nmu+cPQ/F3zdrNyXP2IA3MWfD+0TPEIEZSOk9aPQQu0kMsXvvCFdUsdU2w8L368sK4HN/HJda/+6nWpP22hto2B2OE16zzEheVZG4syK0/Ka16zKUCsvq6VzbNr2goLvVEaP6bhnTLDK+1cfDjm4M8l7qwEpik9cgT6z3uutZ1lQjCvb5GPEvc5YLnN8LggNOS4475W52Ay/nh2bFXEbbHF/Ws/9pCHLCx3Y+9kAUOC2Rfcv66Rhn/gwsGFczvEqtw3gllhGemfOaUDX+TllRefs2M6X2BIA8vVm970pjq8iyx4cWpU3NOf/vR6WHyDZESnyZZco0ljyjXZCKGVy+9617u6T37yk3UFdeIJH1pOy6o1K9e5dJL0Y32jS1+mztpXdj760Y/WThtuwsXTQb/yla+sVlHzSmNFEhaX/N1X37GwdDDC6ZA8c42YeRDNVzSHjkx5C+d8WWbPguMGG25Y/Vmo+x07eaYnOBBjq4TjTJd4aPm1yOqSYeqpEDqy4RldERjp6adcXytz+OhssQ+SHDyQFKv6LTyxQAp+IZhkiUc2N53tMzgkr+Q7mwhmvwzKkbLA0WbqX/7yl+sPDBa14N9fOY4A8Xd40YMd5haz+XHgh81jH/vYan0zN8oPqrS/5EUH1xaRsZTaAs3h2Uo77ceNzvJMePxm49mIiS+D+CFnpMO0nDwHa5fh8J3LjzjWS9NB4JA2rqyxAvfbuXDxbin4zMY6nak6p10457o+u+W5XepD5BMJ5nh3Nw5f8Ru8yQdnXNKPMPs8+yGWUG3e1D2LfBg+WDYt8BmIJKEvvH89nlWu4MLBhXM7xKrcN4JZYRnpnznFclUJporskzydeypipDkWYeZbIke2o7GCNZWcF6gFBhYcITeGiqZCbqKrstCfbNY59+STx/pi3qIOQBwvcS900wLMl7T4BcmSr3DpESZpIzPylMWckXeVNF8qnTYSKj5ixNk02QbiFkPY1y75RU/6OThhDo58Ojn3ia17DtlB0Ay7+Rxj5Coj9/oy1xSGhjmlcaRO5SceS5RVrR5sRE8cepNl8/B3vOMddZN7lthgUYUv4Q/5dK+b4RZZZMKYXNiYGmFhUn9qhHAk3EIrC0/U/cR6px+9yXFMt+vXi+vZRDCjO4xg5T7twI+rWNz96BKujdkO5HGPe1xdaIJgps60fend29aKxd6cQZ9xe0whmdp38lBnNe8Sv3jW+rKoywpp+2rahioL6egT2dFBWkfa8XTX8XTIp7/27/kyncCPKT+yOOXyTJpj7utPptLAGv5cyl8uhvjlPQAjdTCbsamFbH9GjoB2k2cw1+6XiQVz4iry63tVF79Brzcgeldfc2135BFHVmPHOT8/p34FSFs3B9MPXx9F2WCD9Wsfxu+6Iq/rMxFkuHBw4dwOsSr3jWBWWEb6pw6RjwOeKh+8CEeaU0+YOUk2GbbFDmvIxPy9eH3/OIROUo1tsk6jyZE0OjNOB3jEEUdUYmuYEEFMmDzMPTM3MHPQ+EU/L/Z00vxdX1k66L8Vy8Q+Zbjf0Hv8IxNR1akKR6CERya5fT3dJy+66liEh2i5JjdW0/33379uL5SV2PRDkn2HHEm3yGdiWnEcZPkWssUHiH7mQvKnA0uTuZj5IlBfL7otyZGTI+Wy6b1vzrOafqssioJ98qOT4XzzPi22Uo5+mPyQEdiJuzSc/Dn6u55NBJPesTwGfyTHNBFf7UEws8AKubHQz4iCRWmwT33DW9lh78cB7E0z8VEGFjnWBT/I+puCJ6104ttc3y4NvlP8n//5mEowE8eZbM4PoFzLd7Y67dqOEn7Ier/BPIvUPL8IOesvkukdByM45J3hfmLb69+LG/xmK0ZN79EioH1oE865rm2qvMKWugVzIsGsRR3nFthh0Xbg67p0zRb5MDadeuop3QUXXlDZXylKGXWbW58VoyT3WuueJZW2X/z9Gbr+9dDzOhf9Z0gg+UOsyn0jmNeB7GZ7zCkA15qfCP5UJffT5/qGZFhUYHNnDeq73/3u8AWrozN8ZmjW/D8usqba4XhZJ21e2O4d5gCywhgq9/LPi11+yNmee+5Z54EausrLX1h9YMfk0kc6JNBhPqFhclu/pJOUl07T6lBzOxEo25Bk+x0yxamdxZhu5rDV+xJGfg75XVGGM88rcz4NyR9//PGVHGaIM2VAaHfbbbdqOd2mWJjiLx/X/fIgfIbwrAZ2TW/xOIThXve6V2eREMuTrZborTzk0JEsTpqk4++In7jk2huUJYtVh8WV9Tj6KBu5exbcWXwNhSDGwjnhZMLZdfKtgdP4p18m16ZXmK/q04r0j4O5LWacM2e1nzbxCij1V7uyLA0Hv2Asv7+UOj61YP/+MgXhG6X9BE/WYtNDzKn0Y4jrY+9eHSKY6umcc86pm/xrO6zbLM4blqkY5hGSqX5SR9qVZ00b88UuBNbzkPDgJA/4lIpeLH31nwF/4JG2TZ3gk3JERdZ6+BgZMLpgdCNxtA3vF9ZfP97MXRWW9hAs+OU6cp35JW7O/fB2fetFIG3DOdfayCgsmKXljb1HvLfKvOryLPzPAQfWH5oDojinW7lMb7rjHVfvnl0W/W12300H9FFXMvZMVxaZ6il6CaKb5913yS+95NLuM2Wu8g/PPGOwo0zJyrt+pZVW7J62x9PqVkUWnPqhRh9D5VV2JZx+nA2eDWUOBp7R9B2y5s/l2XE7xKr4N4JZ4RnpnyHBvDlSVVL/BZwKLnU9aANjwr04xdM5f7SsHj+6/Lo3TKrCEYqsvNYRIUn9BpFGMVk9o0PiSy9vnaT5hqeUeWgvKPkgu339EVybk5vDaIsfv5TqKvtSxnQUZEa3yNehmMzPKhpLj86W08h1zCxzOtgsYOGXsoujE+93OPS9quh7WdFXfiGENilnAebgFlxd091wJHLMYsqRI728cg6erFHwtk8oXKKzcIQfgbBdDfLhm/V1bmqRtXwJE05v8vt5SEsOP0OwLGXHlm2JDMX7QcGSRg/lFdcQoWFDVjHb48SCJo4jmGSLJmkczd04Av06gbW5TRZ12Z4ICVJH6s/Xsux24AfQ/PnzK+YkwzhtXj2Qx8+PKJZo00xsd2QeoeeVHE67EI98P4b8mPTj67Vl8dBTyn6PtgXT9skMaRVfu5BWO55Jjp7wywru3DsrR9+Zb8pqiVCz8MJA2Rzrr79+nYIAq4033njYttO++3LadUNgKghoi9qYc67dj4JgWhxqNXfdWaUQzKvKs/DOdx5UrI2nlpeEH0MDi+KK5dl9wQue321VfqyuuGJ5viuDHLxHyBARxav9VTnX9395/1zy90u63xRO8MXDv9idfc7ZNa/ll1u+jKqsUj64cef6wZBNNtmkktZrTNYscvTLpF1bhtY9Y1deOdjFwvts8DwNfrituELZjWG5cUMITOHC0XuIVblvBLPCMtI/IyGYamrw4fpxi5PG4+BUukPF8tPRGQY3oVcHJUxF63jyhRBz8eLSIHK/pDNZISPpIG3/QcfauEtHyYr5xje+sVrVdJicfHRwhgutpLX6O/tACo8s8jnnXCvTcccd1/3P//xP/ewiWTpP4cpnAQvCx2rBgoO0xZppQQuCJY1O3MEZCvfpRh0VYsbyhxBbaMGaNlEfi6KswLfVEnnpqFNmZC8duHrQYf6uDOUhxqwttq7RYco/dYf8If62DDJtAHF1RjRZrJRHGaWRFknNZyf9kIALEovAwllYMMt5wYIFnVXvFj3EapyywSHxXMf1w+PXzgME4JX6y7NjGgVSaHGO4du0BUO0FlaZbqEdqm9WuPxwISuru9OeDAGbLuD50ea1DUO+rHLaBkuDuZpnlR+Ph5U5iNquujen1w8IdZeDxtqOfOjqWZlpdUu3kHH6eq7p6ahdVdE7zvQP0w8sZFMHSetzseaSWTUOZ+8YLuVOPUVOOzcEpoJA2pFzrmt/UrqqmztErpGzHF5dvq5TesnyfM4r0z/eVfrvk4v10YiFZ7YYicrzsNVWD+i2KTuBGNKeV/w8GcglXeh1tX6nbKJePCr59ayf8/NfdMcU48MvfvHLMqL4l7F8umLQWKfbvjwzG2+y8WCxaSGdnkPyzMPEVn1i8kc/+mHZ+u5XtU+EGUPIRhtt3G2yyab1XSKuQ/5cnjW3Q6yKfyOYFZ6R/hkNwSwqqSgvVJ2FF6+zo99ZiOOe1ZJFzPCRxSWJ48Xrixbm4VlkUh+QIoMjc7JOPsk7MtzzjxzkJ5tG+6638L570YteVIkaa2OsFB4gTZQcx9CVa/MwlcsCFkRQJ86lo9c5y4MsxMxm8vPnz68dDWKNxPVxI99cVUQSaTNf0qbuiEHKkvyRYNixjCCYiCYiSUYebGnoQo/kIwxZsC/lp8rnGVleEAEdaPBI3biXBzIIEx2kA2mWj3CyWaHUKasli6ttnJB5hCPWHPnLmz4WfFgApd69GGBDlnDH0ClLaTvBPXoNw9vFEAEYwTYYutfGs72QOoefNnLfYnXcqWyTY84zcgl7h3YqjuvI4ueeVf3y0g4RTNNc/ABivUQuWem0R23Wjwo/LrQF7d2OCuYdph3QTx6DTmMw1zg6DwszAy7SvnOmkjZMd34OBN4zbwcE1vr8aE08U03sLWsRle2c8vZIeZ2bawjcVAS0QW0o7THtahQWzEHTLKMN5YfngGAuVwjmwd1JZRTwqqvGR90YLNYoFscNyzsA0bzbXe/WrX6H25f3QdmRooRx3iWem8tLP+G98McLLqw8gLHJe0T/pgx3v8fdSz+2eZ2edYexhXCeJRZMTwoZJWJZg/DrMl3sG92Pf3RWd2ExyCDC9y/GG4t1fXZ4oK/3zAAbOuRZK8lrXhWr4t8IJnRG60ZGMKmlo0hn5F6lzpu3XLWIsIronJAQnR1CwQqSytZ4fLIRsVu4cGG9FkaeBqdzS1yyl+i0nrEHjgxO+jyAyBsieMghh9QOIfrRUeNFcg1ns6jetnSYOgQ6Rgfxck2mPMj8YdmOxWKf44oFg0PW+pZMMjjxI0N6DjYc/5yFySfpYEw/fmQgBeZ1sY5Y1GPeZeQlXs2/yFyxWFGFOciLVde1xT5WvLJIefDhAXN5JH50in50cESmszLIN+ldp/ziSovYkO3QDuhtlaB7Mvry5Zl8yBIev3rR/lwvAsHJWZvxA8I0DltS+QHAX71bOa6dwx/u6ltHYTP+1Je4rtWteuWQpPeX7XcQTM+zucDSi5O8peGQTnOQ8ynEyBPmOvHpkzTCZppL52c0InrSnX8+HsB66cegcHgg22va77JM/zDlxsgCf1Zhc9m0eeVuriFwcxDIM5XnSfurbbC8Lm+uBdNjzGro3VB69fJCnluGyA/sTi4/Hut9+SuvWDFXK4YHoxpbFEPHeuutW4jmmrVPKJFqEa8tsi666OLul+UH6PfLFBrz8/912b8Kgf13lbPySivX3UQe8IAtiyVyw/pOyfM0KFfJz78ijtFFn/WTn/y0jPj9uT5bW26xZbfdg7brFhYeUXMcZDt8z5DBlUe3+lWZ5b4RzArLSP+MjGDWl2Z5YXKIAKexa5QhKYgFq5ahOvPt/MoXR1ovWfOSLPCx4MCQUuSQ1b92f2OOTJ1qOqyQsj5BYWmjiyFt89LSQWpsXvoWuBim36es/r4LK13JUOdKZuQiTnQ3zMgJZx1SPtZAlgykM7qkrOLKQ16R4Zrcfhx+HD8dt3D5xZGLUBqatFmz4WpWxay+F5d85e6nJ4+fehFHPiylhrOPLCvsv17m6NlGio79ONLFSdPXrx82MY685COOQzvwY8KCnoXlJWB+TRbHJK0XmuySB/9+/fX9k6adBwjAWL1x2vnfSt3uV4bAEcy6onmsDmCufSP4iFDadb++yEhdO6eexdFezKv1DGn3qePkLY6OhoX6ZS97WbWAGz5X/xxZeabE5ZJXvZkhf4LnxPJH13NLR+mZYb1ksYeFMHiaBoNcP7TsK8tar+zKGpmuE3eGFLepMQsR0J7SPnPtfhQWzPKkDvudOeZcFtq23377VwvmnDnzSptesRzlu+GlbS9Xhr/9ePp3ee+sdrtVu9uX5/32tx888/oTq8L/+re/d5eWZ+Rf5UfpX4sxA7m8qszxtFBowYIF3QPK8PqGG2zY3fVua9av6s0phirvlmuuHozAqZ5rynD9csV4hZz6uph9di+88E+15u53v83rx1LMKfe8zZs33o+KABeuQFafQ/d8GsGssIz0z2gIZqmpLISplTVWgbVRlMbWb/CGxRGvrOBOZXsZW6lsyxMWDws9uLqp6thLeLIll1+fVPavdX7yFAf58tUYFkcN1X30Z6UwT9LcNMN7VlVLq0NIZ0guOchbnHtzJBE0c88MD5977rmVzOp4dPjSpzPvd7DRjSyyyRVXnORLvk4bATaXk+XJkbmLME8HHvxTpv5ZHmTlcG+Iwipp28qYj+kHgGE/JDllF4/cYOCeDI6+4glXPk6YfMVHaOjssBjJcOlapRy3n7DxduRJFxn1YuxPytH3a9fjCMBPPXB+RBl+siDHizjPgh9wvppkMZsfUvFPvUpPjnt4R2bqVbi27Hn2A+3nP/95HZHQXsjKLgTyIH/nssqeBV167SQyJ+abOs95vFTL7kp7pmee8zyPNBKGuNvr0nvNOyTtVzu3Kt+qcc+rH3/CUn4YauGGD4P7sitly3k2I6Bd5ZnKtfvREUz9ZvkRWGReUxbW7L//AcWCeepw4c/KK69U5uXfvtt4w42KgWOF7oKyVuDSMt/+iivKyEYhg54ZelkopB9kvV+hGGaWW36F8q64TbdaGQY3pL5gwfxuvdL/36ksLvWjN0PbhsYrwSzfKEcHDYUrH8OIqTh+2JmmgvDeoxha1po/v/YznjUyStThc5l3S1Gn+rn3HDaCOfonYDQEs+jlRZuKo6b7fkN3be4Ws7ptSywqseDDi9UvGx0PgmlPRp2f9JEpTjq2yUAgr6SlU66l1VHUBqXFFcfS6KsmOmFDh+LWF38JZ3F46UtfWvVC6qID+eKk83XNL0c6EJ2NrVysLPf1FIQte1Z64Ogiv+yFZ96aPMjVeSO5yGLyShgLr47LkBu9Bg/i+Hyw6KGc0sgn5aYbP3FSX+QL5yfMwiKEwUp1JJmlij50FUe5pHHv4FKPwUKZBg/3QC+Ew9xNc9FYc1irQ4SlD2bkkZGypiw5B/PoLm1ziyMAKzjCyNCtucas6l7EwvizrFnE9sCyj+X8BQuqf6QEa/dpK+pEvafODAu75mfhmXb+rbK3qVXj2rZn2BwoU03yRS6y5K2t5Tq6yksbcM+JN1McLKMznVxzngkdnE9ueoe4T/tXPnOiLX56zGMeU8sWOXku0s5nUllrwdqfWYdAnus8u9qUY2QEs4wozS0EkzzP9zsPPKhMMftBadfLFxJ5ZZlGtnJ3j3veo3v6055WCd55ZY3Deb/9TffHsojXj9y//OXPdSoJC6Yhdn3bqsW6yehw17Ln8rplRAuxNMJx1b8ZVPRdpd+vFtFBP+P58VzpA4RbQc7Vctb3XRbzDKrPG4S+hY6WLAc/khPf2asmuDWCCZHRu5EQTJWUDk3lc17C/MzV8mvFtXgWe2QBiM6JXxqNX/h+9ZtXyE+Yg3M/FSedF7h8vdD7rv9CN2SIUNlehO7JVxx6WNjiIUCYogtZwnMvXcqX8gsTR1nlwQqog2fNlJ/5p1bQsxr6RYdcenCliWxD3g7zthYUEmCupT0LWUPsuZgvHXk4rip4yzv5Rw5doxs/bmIYnPpp1Yt60nlayU5P+traxrX4yoJYRJ4zfy8OLwmLf2zWDr/58+dX3emdYXykOEP5VUj5Q08HOfnRcTVMxsoVXMRPOZO2nccR0PYMUxlV0K684LU/1yFx2rY6sg2Vugj2ME5bSHshWXhIlnB5aK/qiksb1244dWunAfKzab46JVO6PPPuQ9iE55nq510FLuM/aZfUyLuI9db2S7Z9Mhcs5fBjStv3sYNdytSVO5VFfJyyOZQt5XOum0aXc3MNgZuKQNrVxDY2CoKpaZaWW62GpfWWd0FXCGaZg3nyKeX5138vV/rIVYsF8q7dE8p0G/Muryo/tq688or6A9Q73LU+Ku/2OfrzItiohqHxlVe+TbFolrn5YwYQOAzeOeOjd5WcVjpAl8HoivKSudj7pCw84nwaue6bWfRHVsXl8uy5DW6evmbBrPCM9M9ICCaN0kG5VtnuVV6u3SdMBQuLnw6Ln3svaffSxa+mK34a5FQcWfLRqWqEuU7+Orrk7Vq86NvPJ/ERL/HFc+7LHJZHqy16Jo0zJ66OHrkc/KL7S+30WXFZTkO2yZHGYUU1cunMUolQ6rQRXkSODskXdnQf7A820D5y3Il3Q05ah/jK5hy5IZv0NgxBd2UxBHJxIaDi0YODN6uWDjbkIkTTSvnoHdxCKCLDOfmm/lNHtWwTML+h8tza/fMc9esfJsFWfWmV2bbLvTTCOYt8SiOo1/zjqk/xJ5d/0qQd8k9bcB2XfOOn/lOf/NxzVa9yn3hJPxPOyhAC6Rn5bbHQIJam+pgS4xmmv8OPZJZ6i6fshhFHRsoWrN3nSLx2bghMFYG0Ledca1cjI5hjcgcEc1F3YLFgnlQJ5mChmne7xWxPfOITuo3L6Mi8krd59OVP1acSvFIocyfnFatn8S0/NMu0k9Jf1GHwoihDFJ2XK2SQc4+cIpZ5tpTtisuvKHtn/r0+gww44Qxz63ZJc4sxY35dN0GngXHCe2ncICQProiqutXnr9w3gllhGemfkRFMWiEjOh0kY7wSx4lNGr64/U5F48mDkQ5N+r5/CIe0k3VVVmlFGhmiIs9YzVh4TDJGcjLsJ096hJi6j9WFn05E+sjgJ4+QMtcwkIburtORkpvyZHhRWAiUydGlVx8WTd7Rg3yya3nEKmFxwZEsB8dPXPGiW8WXX9GLiyw6iWeRk3QTh9uTV+RJS69+ufhx4oo3+NU4rmPKSX70ETcY0S1tJvmRRxZ8kpewlJec5q4fAbilHcIs9cWfFRPW2ihc+3irB46fuGlHabvSueZPJhmcoWH1QR4nbVzqLum0Dbkkb/7RQTptwjGTHB3p5sw5m3Np1wVD48G3DvuVURj7ilrEyGLPmh9cpE1ZyYBjnr/4i9NcQ2CqCGhP2pBzrt2PgmDqluqcx0LSukVlBOLqayrBPKXMwUQ49SmrljUTCOYeezy127R8KpK7plgSry6Ld5DKahAp74fodk3Rk0Uz/SsyWddbFJ3L/0pASxQPW5E/6EtStgG5PK/unWkql/7UMyjckL3P0VqXYJTPO6st8qnVsUz+jIRgqth+p1Kbg1ZSXBqF2zpJeKyY/PNSzXX/fixaPfFPWN9/Sdd0qp1WSa+tkqERs9xE37zgyfLCp8vEDiEdrfRJl85VOjI9LMJCoCKv32EGi355+9fS98vqXjgHTQ9ydOCfjth15Ez0cx85yjWUV3SOS/jEvJVbeciIfGn4c+ILy8MNk+iSNMmvJih/5CVMWulc5z5pxU26GiZdKWM68pAT8Zq7LgLBEcZccHYPf851wt3DOZi7T52qf9ecl3WekX76G5IZ+eSm7vgN20CROWjdVfywjXmeinIDzxnyVxmUHUE3dWS//fbrjig7Lphak+cBhuZb2u/SfrSZ6qO9Bs/adsfKLV3acrCaIcVtaswyBLRPbcg51/VZKw/YKLYpGjypg77z6kowD+5OKV/yKdnV59k0GItr9thjj2LB3LBugVdHQsoTzphT2/nYO8Y2ReUBr/3ZoqJznvS8R1g7XStHdV4HYolYvBgdLr/8X2V+5x+7n5bFtKecUtY3lA3XGYAQzJ122rnsrrJ1Gapfv75G4OB1Enl51ogfYlVENwvmAO5R/h0ZwRz8whkQh74FSwUOK1Et99xYexn7dTRIm+BhY+AxIV3iLOmcRjpoYBrZIP80ZB3CuNOBDBq1DdXH8xz395BcW1az+e7pxI4he0qGYF5fuft+4/nKavBioFclVGP34vTTpBwT9ReHX8IjL3n0rUbicuJwE+XnPjKVM+QjaZK/9MJCOrIRPf/oMDG/lFEc6aR3FEUqieTPJZ38OenSIdf41bf9WRICqUf1kYNf8JU+/q7jD2/X2jPcuUH7H/hPpg7I5chKvUvnOmE1wtgf+fV16YdNx3XKKk86cSmXsGF48ffUIInmUFst7utXdlrgpDV/3Bzp5zznOXV43DB5yuicZyTtOfLll3hVWPvTELgJCGhP2lHalWvHKCyYRcxQrufg2jLv8sCDDqoLV/WZ+kNzMFnsn/rUp9SV5FZ9I5hVpzzvXgdFQCWYRai2X9QeuBLWfwYNryesvkXG/1QhwozSnHvur8ti1O/XL/JZL+B9NSCY25RFuutXvWXgR+vweVag4sgIbnwawaywjPTPSAjmSDVqwhoCDYGGwFJAoN+h9Qlg7ZhLp8hv2FGXe4vxbPVk1bh5l1nQpKO0r+su5VOn9hS1G0aI5FIoRsuiITAkStNBMMHrWXHkeXjnOw/sTjxpsGOEkUl7Xa51r7W6Jz/5yWWHkI2mvUYGvHRR3Vz9Zz/7aflQyNH1xx+CuUvZucLnKn0ZLqSyEcxpr5LrzaARzOuFpXk2BBoCt3QEQip1SumYnQeWlcEqeWG595Ue8y5ts2aYTofLqmlLJpvJ77333nXPXCvndcTNNQSWFgLarTaXdhwiOBoL5kCu54XzPJgiYuu9tHMLauwQ4qMCtqCL/3SVH8EsYwx10andHHzw4dxfn1stmLvsukshmNs0gjld4E9BbiOYUwCrRW0INARuGQjoiGORCYHkx/XP4rBcslh+6EMfqsPjrtOB63R9AtOetCyYOtosXLhlINVKMRsQ0Ga1Sedcux8lwfQsxPl6F4LJyScE80lPelIlmJ6p6XR9gvmzn/6sO+roo66fYBqTL/+bBXM6a+OGZTeCecPYtJCGQEPgFoiADlhnqRPUOXJ96wyrJMd6aVGPzepZLVkvfWc8nadwW3K97nWvq4t7bMVlaFx44lRB7U9DYJoRCKmcDoJJ9ch37Vk5aGwOpmdFW7dbglXbPv/rIw7T3f4XI5hloc9RR/UIZvmht7VFPuuuV62cnvAyI3X4wzHPvN+TKVebg6lmR+8awRw9pk1iQ6AhMIMR0KnoJNMJxpLJX4epA2LxsGuDfWrNu9x3332H3xnPYidfptp9992r9dKnWmO5TAc2gyFoqt3CEAhRcs61djgKCyaoItO1Z+TAutH6ycN5ygimRW72fjVEPt0OweTsy/zTMgfz6DIH89dlwY/vkxtJaARzgM+y/tsI5rKugZZ/Q6AhsFQRSCecIT8dsYM/4hmCqCO1mfqRRx7Zfe5zn6t7xYaM+kKVT2H6zrgFPuZdsmjGEtoW+SzVKr3VZxYCmLadNj0qghmLP6A9FwcffPDYKvLB1n6GyK0izxB5nqHpqpghwSyfoMwQua/LNYI5XYjfNLmNYN403FqqhkBDYBYjoCPWUTr397RNkWzebI9Llkv7XdpjLwt7kEdf6rHnpVWzOl9yfIBBnFxHVjs3BKYbAW0uP5Jy7X4UBJO8PsG0Jd+73/3uug+mH1SOug9m+dHledhkk02GowPTVe7FCObYEHkI5s672AdzmzZEPl3gT0FuI5hTAKtFbQg0BGY/AjpLe+gZIp9oaYmfL4TY6/KYY47prFLNV7l0pD7ZynJpcY/9LrmQTPLIyPD77EerlWA2IBBS6ZzrURFMckIyg8Xb3va27rTTTqtffvMlL1/q8Vw84xnP6O5zn/tc57lKulGdl0gwyyry9dZrczBHhfdNldMI5k1FrqVrCDQEZiUCLC4IY38LonTMvln8h/KFkG9/+9vd+973vrq3nu8dcyydhsNZL32tZ7PNNhtaP5M+i3wmEtdZCVRTetYgEFKZdqj9jZJgAsKPqDw75iXbTSFf9/Js+NjA5ptvXrcr4j+drk8wz/7Z2XWRDwum52/nnccsmIVgxlnEAxsuz6bb4NYW+VRoRv6nEcyRQ9oENgQaAjMZAR2l4fGQwdop6zzHOtCvf/3r9RvjLJisMzokHah5Zva7tGp8/vz5tUPNMLvyRl6soDMZg6bbLQuBEKXalgtzGjXBJJfzw8y2Xcik9m5KiGcjbf6yf/4Tg6tD5tOJcAimaSw/O3uwirwSzLmNYE4n7lOV3QjmVBFr8RsCDYFZjUA6YdaYdJw6SGTS5+bs8TfxO+M6bHMuHb4zbr6lDtbBxbrDKhq/WQ1SU35WIaAda6Np26MkmIDwrJDJ9aeDaOvJk784nqXpfgYawaxVMeP/NII546uoKdgQaAhMBgEdHKeDC3FMp1s8yoKHwVd6akdZrq8qVsyk4feb3/ym+8pXvlLnXp555pm1k9Sx2uvSqvG99tprOO8ynaq8kh9rZobdJ6Pv0oxD32CRa/mHFKAOtmXq4zYV/YKHNPKpGPcECIdl8BLknrsxMnJT9amCp/lPX7e0o7QFWQeDxOv7DXEvWAnPIX3STUV96aWLHNeOUSzyoUdfX3L7dSdP4c7qUvh0upBLeSxmwfxVGyKfTtxviuxGMG8Kai1NQ6AhMOMQyApuQ3bpaHXYnA5Qp9gngO75c5deeunwO+Pnn39+3WBdXHJsIL399tt3T33qU7sHPOABRQar5aAzj0ydan/YvQqdKX8QgHLQMWQguIQouJ9XDvG4hE+mCJHpnLQhGZVqjBESw6vqJoTyaivuSwI4i580kUOW634Yv5ng+joq49VjZFlZ0qZgKF4/Lt2VJ3Fg0W+H7qeCfbAITskvmI2CYKZekteyPmszBdW6efqfyzZF5mB+5aivdIM5mPbBHF9FHl3VUeoh5dFcg9ugDhd1J5xwQnfSiSd1P/zhD5O0Pu877bxTWZleNm9fb93F2mlkDSO3i8UQaARzMTjaTUOgITBbEUDwdNwhLMqRDmBix5vOpvQwleQcfthh3Ze+/OXuuOOOq3PMyLEylvUSqXzRi17Ubbrppt0aa6xRO6WQgJBK+aSzskl7yXjGwRhSQ9eJuASPhCV8soVIeuf+QU6wQqSQWJZS/uLRySFO8gzpDRHln7DJ6jOd8ehby1LIIDdRf22C65NNZUGw418vyp+J5SKXX8qeeEs6w1K6YO/acUsnmH/685+6n5z1k+7Yrx5bPxU5pyzS22233eq3yNdeZ21MtLr2qcgltaDpCW8Ec3pwbVIbAg2BpYyAzlkHO5GspLPVybDQ9eOwXP72t7+t3xn3OUgrY/sEYeHChXVV6mMf+9j6OTwWOPIQpXJRiUbyS+eu2FMlCNMJVQhRX09+yoEERW9njr+4k3XSwT7EOvKknyhTvn0nL056bt48Vr96Wf9E58QbD1l2V8Ezdew+1ykH7eie8gcTZ2XJEZwTLp2w+LufjIvcyIn8WyzBHMMxX/I55uhj6pd85s6Z2+266671Sz7rrrtuxb+g3b5FPplGNA1xGsGcBlCbyIZAQ2DpI6Bz7Tsdv2FznS0C4EAexUMUkYFf/epXdYuTQw89tDPvUlwO8WLBfO1rX9s97nGPq18p6Q/BhwDIw7V05Iackj9TnHJawEQnuioHvR2c763HTxmU23fVg8WSykG+9PBNmj7hggl/W9f8/e9/76w0vrJY8/jR6Ta3uU3F27U4dAyW6iGylqTH0gqnm4PLNR1dw4HOygbfTDuIvzTBQ5oQ/GsLhobZU0fiTcXJW57Rx3XVoah5wgnf7E488cTujDPOGIoUZjufbbfdtttgA/tFFmI7dog0UQ6/meKMOWTKx0UXXdSdc845de60Z3leWUVuEZ5PRW6wwQbNgrmMK60RzGVcAS37hkBD4OYhoDNEchATB8dPB89fRx5/9xw/cy3td/n+97+/zt+y36U00trv0opxw202jkZ8hOmYkYLEI4tsnXM6vXTuwmaCCxYh1yyNc0v5L7jggko8TAuwsbx4cHnQgx7U7fumN3XLTZIkSxf8ldd9HwP5Cufvs5swP+ussyqGq6++eiUCPjHoe+5JD19H6hTmM8UpR8p47bX2VL3qOsQ44Wl77jNEri1VklkKtHy5FrZYeZHDsXY82TInP+dcq4NbpgXzhghmWeRTcHvUTo+qX/LZYP1CMMec5xMuHFw4t0Osyv3V17Q5mBWYEf5pBHOEYDZRDYGGwNJHQCehgw6pqR1ruU+HEnIpDuesgzckftRRR13nU5Brrrlm/c74C17wgkp+7nSnOw1lpXQTZUeHEIrEm0nnED340PMXv/hFx3L7pS99qX6tSBlWXnnl7tGPfnT3sY99rJKmSekP/3IMO251gXSPdeTIZQjUfvvt1x1W5rv+rHzeT34LFizoHvGIR3R77rln3aRbvP6PADKi76R0WQqR6E3HWj7XRed+vYdMR/car+iFYEobslwthqV8A9ozID4T00y2OORK65xr97d0gmkVuS9tHXvMsd2vfv2r8kNvTrfro3cdfioy+DWCGSSW7rkRzKWLd8utIdAQmAYE0rESXTvW0ulzOv6E6fjd+864/S7f8573VIsaS2bIpzQLy7xLlsunP/3pdbi4WihLZ82RhajFsuYs7aJyIFl9olETzJA/dAwucKD3j370o+7DH/pQ961iUTz33HOrpne4wx0qwfQVI8O1k3XBOPHl5eD62L74xS/uPve5z3UXX3xxDfc1pOc85zndQx/60Eo2xTecTD9D9VyVMiareizjP8pTLZBFp7SD2gbGSN7EssMmDvbKp2wsybbK4siBd+IGu6Rb0lk6aVIP0eGWTjD//Kc/V2v4V4/7ah2FUP7HPuax3TbbbtOtvfbalXDCrhHMJbWg6QlvBHN6cG1SGwINgaWMQDp5JE9HzuloQ3B04pztTL5cVozb89J2JJdddlntmHX6d7zjHTskiBXPd8bT0UeGDgy5MMwpn5JQJlWuP4lXw4a+y/aCTtcUnSsJKRjQDck5+eSTu4MPPrj7wQ9+0P3xj3+s4aYG+Azmm9/85kkTTPIHhAsUc7vl4DKGCeLNwiecBW/vvfeuFkwkH7YPfOAD65eRrNBfs3zLOntxJp046i1Wv2WL5Fjupc5jsVX2im9pb3TN8Dc/DtaIZIa8tUvzYW3Ur1y1ToSXg0u7nWp5I8c512TeoglmoY1/uegvgzmYR36lzqeG6WBLoW0GYDqq8gAAII9JREFUczALpgXdRjBr61r6fxrBXPqYtxwbAg2BESKQTrV2rEWu+YW1gy+drY49nbbO58ILL+y+//3vdx/84Ae7s88+u27UTBXx58+fX1eg7r777nW4FllIx0825yxuSBTiVElEkS2uvMSZKkGowqfpD33oFRzo5tqef694xSvqXEyf/1MWRM/cU5/DnGwZ4CEtfHMkL5jI/59lYY8Vv/I7+uijq5/islwedNBBdSN7n+IUFyFD4PoyZxphR9Bj4aVnXMrr3rV5ruedd173y1/+spLKtdZaq257VctZ8FIu8eJquy03Uy0veck71+5vyQQTZhdfdHGdP3zEkUdUgqnN7rjjjnWbog02HCzygW6wkSZ4F8hqe3MvTpuDCZ3RukYwR4tnk9YQaAgsZQR0qAhN7ShKZ4HkpKPOtXBEALm00OQjH/lIHSqXBlFgUXrwgx/cveMd76gbq6+yyipVhvTpkHJWvOSJaOjUQhSS71QJwnRDRl+60k+Z3Jt/+pSnPGVYPlbFze93v+4xZUumV73qVZMmmGRFbsohLy4k7K9//WslACym6gA+6uQhD3lId8ABB9QtoOCojoIduXR1zCRHx8svv7wOc9NZOegIA1Za18rPMn7SSSd1xx9/fMXaXN6ddtqpe1NZQAVr5fMjpl++fhubSpnJktY51+5vsQRTwQorNAfTD8Wjjzq6O/c359Y2a3X81ltt3WWbIjj60QIXLhi7HWJV/BvBrPCM9E8jmCOFswlrCDQEljYCtVMtnXuGId2H6IUAxILGcmlo3Gch0+E42zvP0LC5lxm+DGkUHhKhcwqZ4J/OSpltNUMH4X3/pY3HxPxgEf1Tlj/84Q+VaNuGKYtPpGPBhMNULJjSkZuz/JS/jwFShlT97ne/q1sViQsncz4XLFhQiZb40TNpI7dPwmpGy/APndK+Us4QRtMsTDdALM01dc2KyXpr25xYh5M+bYVMR5WnbAWLqbikVQ2LFsFfG3TddcdPYpuimleJm2wnypmKLtMdtyBV8B9gdf7vz+9OPeXU7qSTT6q7QsDhaXs8rdtq6626Ne5cPopQ/kGSjVIYN962bohgnlimzvyoxu3mlNGI8mNop0Ja25d8BpBM5W8jmFNBq8VtCDQEZiQC6Two59qRzt+cN/MubcfzxS9+se4HyE+4L/VYNf685z2vWpcWFLKTdDr/yHIOGRAe/xAf9yFW8VtWQNElLroibrEMInv2RbR6HOFGMMUTvt1221USBA/kfLIueCTvYAATeZPPminv4BQCX8kWnUsc8eKiu/vIS9ioz9GbXPnSmUu+0avvX/UucZTDFlc26bcno/0YzWv95je/WbGFL4umbZhMv0DquVrSsfIGv+STc424hD/S0mVu2QOSc5/0WoJ9ME+auA9m+eLNTo/aabAP5obr13RZzl5VKgnHW1GtmkGcGfCXXrW+SjmtHFe+n/zkJ3Xh2AorLN89+9nPrlNcVlyhzHMtG/eHXAaTFGEc88EPwmuvubaQ8RO6E4uF/cwyNzvPCzx2QTDLwqH11ltfM62u6jAmbKLs5HFrPzeCeWtvAa38DYFbAAIhLYpSX/al80lHZBjN3ov7779/99vy1R6bfYuvA7nXve7VPexhD+v2LNvk+CRk/Cux0HGXg0tnU29myJ9+B0eldHLKkDDliL846VQ/9alPdV/4whfqVk3ii4dQsl6ysjlPtsyRSQ5HTt8PqaQDy3B/mJ58/khbP20VAvexnlycfhlq+Aj/RFdnDhb0dK8s/fzjjyzT2/fUryg/VuxE8L3vfa/7zGc+U3/MWCWv3OKRE4Jp034EM+VJnhOLk/CJ/td3z5pHl+BZG/6QBCGY5fvaxaJ65plnFF0GsCKjO+74yGKV27bbaKPBfpHC6FPLO5YRP0x4rCquL/tl4hdMEUtTPTzXMPBjcY899ug22HDDWjfLF8Lp6z7aVx/T4D4o7xjBLHFOOOH4MoXjxIqV6QvKb6/TXXbZdWxT+vUrRsEpgPZlLxNAZmimjWDO0IppajUEGgKTQ0DnoXMJmfLyTwci7Gtf+1odDmaxy2IWce35aBWzDt/wpRXk4kvrjBToZEIyJqfN0omVMqaclRRcDwtIx2dVdliCNPvuu28lmBagsLDBj0OAWNmmQjBTYoRKh2uD9hCxkNShHiWcgz8X/bNqXLpMUeBnykHS1gTT9Ece9IdDdKIjgqgtOMSJLuKIby9RlnEWYdfmml5yySVDcqk84onPgjkdBBOhBKvV+vKhI92LkbLOwfz6179e9bMtVcpgQ/JHWgyzzTYTCGatlGFb0aQcM8mpC20WiT/11FO7ww8/vC4imz9/fvfwhz+8s/UVohkconva+KBM4+0KZsLU1Xe/+90q88c//nGtt9qmiwBTZ7wrvCfkTQcYezekLSefdh5HoBHMcSzaVUOgITALEUgH4cxVUlOu/1FWLptreMghh3THHls2Yi5DlyE+OgXDwbvsskvd79IKZp1FOqHIRDDEDbGYCfCknHTJdV8/1/xziJdwcwVZcF//+tcPN5jvk6onPOEJlWCakxpyKP2SnLzSGcPMRuq+DpT9NaUXJ1gijzuUDdbXW2+9KnpuYUMluOJPV/ESN7ovSYebGh7dLQTh6hZE5YzcahMhmM4wQTAMiSMjtnoyHG7Rj4Vh8wvJcUYybQCOwMMbxtNGMCldsLu6DPEG41qWUhyYIpgsmEiT+ZmlZBVbC462LcO+G2xQrHLFe/D0EDbuyIPK3HkDbMZDlt0VEq9dOUxJYMVcY401KrFEMM3rzedHaakM6o7Tlhx5ztPGhKvXEExknJ+02kD9/OTW5fOT669fcB78CJnYTmsG7c9iCDSCuRgc7aYh0BCYjQjoCMyr5FYqQ7E6AZ37N77xje7jH/94tUogBzoWZ3MvX/7yl3ePLSumFyxYUDsRMoTrdMRxRoR0vNNNcqriN+EPveg90fHTQSqPOCFKfyrbNJlfZmsg0wbEEe5QXp9sZGWz1QsMJuOSl0VORVC1+hm2dNiSCMFCbOUVma4/+tGP1rxcI6V0pAOyxrFkIq3uL7300ust52T0W1IcGGVvSvopD2u2NhJHR7oIp4sh2X322aeuiEdmfF6ThWvrQkLsn2rOr/mths0RIT9spo1glurXAswhhP+8QgYRRo7/8eUZOKkQ4bPO+nHRY2BNVQ4/rrYtFsz111+v1M1gSsmgLdSkBYdC+EuZMczll5/8fNxB6un764eLr3DZ+snivVVXXbXbYostKsHceOONh8+wNsWp39Sdcitj2qM25+DU0be+9a1Kxu2PK64w7RBx3XLLLesPojwv0mgXnLjNXReBRjCvi0nzaQg0BGYRAl7yOoc+MbRy1yILRMqKcSQHcdA52Itwhx126B7/+MfXTgNBuBIBKuFWAYvH6TRmYgdCv+joTMf4sVzZBxQW3FD/cn9lsdAgPG9/+9ur1cfcVPGSXvynPe1pA4L5yEdWOfwm45J/4hq+tIIaufLZSZY+nToiB1ekwDfgDT1KSw91wyED/BAEq87Vo22lEIXpcMmX7JTjLW95S/fQskenPB3i0Ef4v/71rzoUbk6vL0Ih4xtttNFwL0/tCQn63Gc/232pbOhvGx3ppotgFtFVfmWCpQxgDFbmWoZgsmBmw311sOOjHlUJpvmKY9BXeK8pRLW2myJYvLpQZuYYMKvF+JOf/GStD3OoN99884r97VdbrZs3RggRQ3UWHPp1rK3FP5ZzBfcOMV/1lFNOqc+HOHCAga2PTCdYv1gw80x51qrcnCt67U8fgUYw+2i064ZAQ2DWIeCFj5ToVFwjANl70Kpx+xGmQ7nzne9crUzPfe5zh3O1dP4hNemY+JFVO5HSIelkZoqjm/KERLrPocNzuFcmBzwM2Z522mm1A+1jIq5yik+e768bJteZRv6Syp28xXMdp8OGPUJr71HTFfitVoiAPQr/+7//u9t+++2vk0/0IYuVytzZfYq1MHUY+aM6w0BZ5ZvrT3/603WqAD8YciGY7pHl73znO3VYFcHRrhBLOpOBYPpa1GcLyTzrrLNq+uklmDWLmn/qgx7mWmYO5g8zB1PUEmbYVz1vWAhmSTjGT1k/B1Z7fgNyqU0N5M+Ev350IIHwvkv5+hOLsWkJnlHtyzOsPj27fjSmTaZu0q6DU+rd/TetIi/TCQyRc/zE92UvWK2zztqlnQzqGL4OLud60/4MEWgEcwhFu2gINARmJQKlE6jz5srL3pCZDugDH/hAnXfJwsRqhpzogBAbq8aft9de3W1Lp6TzCFnTmSAPIRJIhOuQzpmCTfRUnnSWfd10mDraWAxZEw3psvogmcJCmMlycMr5mte8plp2kY7JdprSy3NRGZclKumc6bfffvt1n//858fmAC6qFuSFCxd2z3zmM+vK/eBNTtKSZ04cCyiiNt0EM/jBhT6f+MQn6vQJ+mgT0S14ux9cI/uDBR/i8ncgxqYHWK2PrPCbboJZ8y64XVvqwcrpuidr0ckiN5vbn3nmmXUBFuIl7qPMwSykiQUT3sWzEk/lskDI0HgRVV0RN6Mc/bla5nJN57QdK/vLzbCNK1t+nOQHY+Lyz/Ov7g2Rs/Kffvrpw+eev0VvW221VTd//vyaj/SOilvRI+2iKtX+DBFoBHMIRbtoCDQEZisC6WgQKfOzDj300DqUhkzFAqWjMJ/Kgp673e1ulUikY9DJ6CzIsbpc58HyJ02/I5kJ+KRzpQvdbsgpjwMG5jEimiyK6RTJSZnJUFYEkwXTkO+NyZ6YZ/DPWVrXcDXcbEskpIsf8ioPViFz5sStpKcITYfPD+E1l5HFlbVT2HQ5+cHCGcE0b9f83LQP+dI9RMY1RydEeMUVyhd5Cn78HRaUIXaHlAVmiB2/6SKYw22KynD4nLHFUqUYJc+Bft8qUwzMwTzzjDPqSnN6K8dOhsjLvNGNN9qwztUUPxjUOimkskBSnHLfcDsTY2k6OsJdGeCqfeeLSLmn/2CboUF9SJO6dK2tx0nj8J4wZ5t11OIh94mrrZpfu84669Q2Qj4njuu+vMht5/J+KsAOnpSGRkOgIdAQmIUIpIMwp1Dn8OEPf7h26uZhevHrJMRJp5BXnjB+wjn+rhEM/iGm8Z8p0NAtZYne/Xt+uXemvyPkSBn7/okrji/4PPGJT6wkMP6TLXdwci4ZVtIi7Utf+tJK+BFcOlgs8cIXvrB22Pe85z2rLv26CJFEMNWpIWZkLfU0WX0mG0856UV+1b0kfPKTn9zd5z73qSL6xETc4OJsYdNVhWQYikYwExaCaf7pGYXYcdNFMOnMijrPRuuIZRnCZdHnioqFpJ/bnVd+eP2+TFHI8Dc9EX3zkQ3vu69HSVMxKPch/WQ4ZopTT57NkDrtJaMMyic85efPKVO/Hl0PcBv8sFR2fqY2WBzoy0vCHcI23XSTsmfuWnXxF3mJnzYZ2cKaG0egEcxxLNpVQ6AhMAsR8JJnxbAnns9AWjySlcg6GOE6Cmcdgc7BfToZnRVS6Z6cdCrS6rxCeGYKNPRP50rXOP5c3y8dHz9HsBA3R+LA4Y1vfGMlmLYPirzIX9I5ssVznftnPOMZlWDKn1VpYRkel8+CsnrfljLyCf7RPfqG5IcoLEmHmxIu/2CgruWZ/EJeyK3tAN7iO8pXYspNKac2MviRohx0RzBtjcUSakUyv+kimHSjVs5Irz0x6bhcWf0tLHimnIiYIXR1pM2Lq00tt7ydFsaJFf/yXzFnjKOzuqAv/QYKDsooTBmFj+8M4H7xZ19hxGV9Th1ngV8w6bdJMmEozFmbSRuZMcDMQEUawZyBldJUagg0BCaPgLmGrGPvec976oIQq8Z1BDoBhw7EWYeBPLjvOx2GMHGkS7j7OP4zxdHLQe+4vq7X559OMfFzPyQcY7Le8Y53VIJ597vfveaR+Es6kxeMIvPvZa/IX5c5lIa3bUYuXNgjyv6X733ve+vehaYjxF8erhGHGyvfknS5KeH0d3ADfAekbVgmmI8RMnFCOBI/aZ21H5uum4NpaoDV20jcdBJMehgqZzbOMLk5sfwMb+fb5HQb6DoglJq4Miq6ctctjgYwEFnDnOdVMu1qZrjgTGd14cz168F1/IXlmv+wXnvEkV/eA+KoM2n49eMP8BofEYhceTS3OAKNYC6OR7trCDQEljEC6QCur+PwMheeQxxDWunMTc5nvRRPGBfCGItkOoh+MZNX5Dr3Ow73M8kFh+h0Q7rGP+Vxr6zBwLUwZ9ZF2zqZH7lamadaAIj4Gz1Lnw6YfAc/2xSxKiP+Fk4g91aQ28T93e9+93Az7KpbycGwJjnqKXpF/yLwRnW4OYEk08HB9fFxnzLxj0UVCa5698orbnA11MqabnGTIX7ppptgGhrnEExOcegT/VVntbQKKDfKw2+s2DWN+76rpLV4zKQ5mNEvba5vxRSWeky8nNOWhAcXGHDuvSe0vcTLYiF+w7xK/EwzkC7pXTd3XQQawbwuJs2nIdAQWIYI6AB0yF7eeYGn0/DyTwehQxDPCtk3vOENdaW0/S5jeUhanUPSp1gT7+N/Sz7DDibIEdxgkM5UJ8qaaHqB/UHdT9aRY3/FzEF0D3MrwG2RYyW1r93I0yp+3zq38AeXQYnoJE0O96k7flz0rDcj/pN8k5e8tSH3IRzJn38JqKux+cU/MuL387JI5MtHHFG/TY5gCp9ugjliWJq4hsDNRqARzJsNYRPQEGgIjBoBHXK/kyefH+KSIVSEBXE55phj6sKekEvx0vFLhzC45x8ZzrcWp+wwSJnhF6uu6zjhh5RVzxa49PFL+A2dg2kwVkfy87lIWyP5oo8hY3nZS9CWL3vvvfeQRJJLhvSRkbzI4kfedDp5aE9wCU7yzPBo9IiOdOlfS9MvQyOY01lbTfZsQaARzNlSU03PhsCtDIHrI5gIQDp2Zxuq24jbvoXmYnKIAULQJwrihgTkfGuBM3j18YAPh/SFvMEWjgjmVBy50pLjGlEzZ+/003/QHXjggfUrPjZZF+7LPbb/ccibblz0Ead+bab4Ce/rPGH0tqYbxR9D83TOUGvIpHtfhnGfI3jRK7jSwX3frxHMUdRMkzHbEWgEc7bXYNO/IXALRQBpKb127eTTgdeOnn/p/FmXDMEedthhlRghpAiKA2EQl3OPDOSeH3m3FhcilDK7zzUMkCZYmoP5oQ99qA6RTwUbuA5I5cAa6pp83zq37ZGh8nyq83nPe179FOV22203HH6WFxnS0E16jk6u8yWivs41woj+aGfjK44HpNbWPTf0LXLtiQuurukW/fk3ggmV5m7tCDSCeWtvAa38DYEZiEBWwFJNhx5y6DpEg59N1W3EbTEFC2bC+h1+CIH4rhGASl5nYLmnQyUEEnnMlizBQ17By3ZB5kfaPsgqbxhNxZHTJ4kIm4VXz3/+8+vXlercxSLw1a9+dV1EtMkmm9S8a72OEcv65ZlSP0il+iHz/PPPr59k/OhHPzptdRaimDbj/Na3vrV+8YkeDnEQXmFc0gSnpI3/jCWYRf/8tIrutUDtT0NgGhBoBHMaQG0iGwINgZuOgM66Eo8xEa4dOkREKeRDPHP7fvrTn9aVughUP106+3Sk0iFb7vvxbrqmsyelMvfJkfK7RzZds9bZmmjhwoXDr5VMtXSRKS8bpPvE40te8pI6l5Ys+R188MF1G6Q1ioXw6jESKb6Dc079urefJDmIL//pcPIMDrm+sW+R00G8HO6VzRG/mUow6dh39G2uITBdCDSCOV3INrkNgYbATUJAJ8iKhQy6Zv1CLpAAn+QLMZn49ZQhwZFrjwDoRIX1CeZNUmyWJlJu5Bs5D8F2z7HKCYezsLi+lTN+SzqT4YD32WefXS3L++67b61L8sj/eFlE9ITypSBxMhRupXbSDuuwhIvvU5GHH354HWqn53S4PsmSJ0yW9C1yekiXtNE/fjORYNLx+lzKcH1hza8hcHMQaATz5qDX0jYEGgIjR0BHiEyks5aB+wydIgBISUiQ+INOsnSg+tDS8afDHw8byBAvZGfkis9QgcEieLpH5DiEKvfOsAmuUy1OSCsie8IJJ9S5seZ0cquuump3l7vcpTvggAO6nXfeufqpT3n2t0Ryz0Un3y+3iOtNb3pTbQM1cBr+wAYmztrXxz9+SFmItPtiWASf6Bc8cy88fjOKYBa9rp9ajgNJ7+YaAqNGoBHMUSPa5DUEGgI3CwEdtaP8GVoibW7s83exbCJB/Q590EHqRse25BmLjzQgUcKrTDHK9U0lUTerYMsocfAMiejjEJX4sRQje/CaipMWziFo0vsGt8VXFmHBes0116zf9n7Zy17WPfjBD65+4nOpS/Vda3CM7JD75z/9qftJmQKBsCb+VHSbTNy0B/KDje+xb7rppjV52krKV6lY0VG6PqbSxm8mEcyU6cawSDluLE4LawhMFYFGMKeKWIvfEGgITDsCOsUcMtPJ6wT56ehj0ewTonSk/MRJvH6caVd8BmYQzBAjBM59H8/gygIZrKZCOIJzn8izOPpMojmU/Ndee+1uhx12qPMvN9tssyHpTz5kpP745YiVc6qkdyrVIK9KIgsu+REDB04761/zS/zoyI/ujvg1ggmV5m7tCDSCeWtvAa38DYEZikA6bedKiIqe9izsOx16HDJSevn6lRV+0iEu5mpaocyFyEwnYakZzbA/wZJafTz7aCZOCNRkixCcK0kriZAylspDDz20u+iii6oYX7F55jOfWUnmOuusM9RBoPjqRb4hc+4RXsPVIcNV0DT9SdmJD0lMufjZE7P/LfLEceaSPv6NYFZY2p9bOQKNYN7KG0ArfkNgpiKg0+ac03HHj386d9ccosL1CYmh9RJxGBdxSZx6cSv6E+z6eBZwBwj0iBIP2A7iI+nj+AdzYZFTBbgvF4bZL7vssrp63PZR9rBUH9tuu233mte8prvf/e5Xh8v7aUP6yQ7x5+cHA8JZiWv0rJmN9k9I9qC8YwSzZMF/Mb8xTPg56OXcn7ZBM+U47bTT6mc37QV63nnnVb8NNtigbjBvRXzioaf50YTERpfgXCOO4s+S8Ct5N9cQGDUCjWCOGtEmryHQEGgIzHIEQqwQPUcseJXslbIJR+gRoRzi/fOf/+z+VOZNvvKVr6z7YIqDNG6//fb1qz62QrLgZ+QEaini3S+7MsfSqkzIpv1Yv/GNb9SFSb5g9I9//KOS0Xvc4x7dLrvsUrdcYpm1GGrFFVesOJIJp+AenJdisVpWDYGRI9AI5sghbQIbAg2BhsDsRSAECsnpE8FcCw/x5Id8zh0jR4bEbVH09re/vX7Gk0WTnF133bVu/YNQIVKxVM5GlJTfYTN5DlFktUUkL7nkku7EE0+si5KOOeaYSjZhIL6yb7nllt2znvWs7t73vne31lprdfe85z2HUwTIGlps3TTXEJjlCDSCOcsrsKnfEGgINARGiQAyxPKIGPYtafzjXDtCOnM2HMx658s7p59+eo2++uqrV4L5rne9q1uhWO7yxZ7Imm3nlN0m/z//+c+7c845p7v88svrYUqArZXs3wkLRJyDo3RrrLFGZz6qVfU+R+kLSpzrTTbeuH5NafU73rH6tT8NgdmOQCOYs70Gm/4NgYZAQ2CECFQCVYgRIhjiyI9z5hf/+CFSSNRZZ51VyeWxxx5bSRar3YYbbliHhi38WXnllav1sp++Cp5Ff2CgvN/97nc75TTXNE4YHFK+4MLKeU0ZPveRgPhJk+uNNtqo22233bqF5UtK8+fPj7h2bgjMagQawZzV1deUbwg0BBoCo0cghJLkkMrkEvJU75EtB4tnGfo2PGyLoh//+Mf1c5GGwh/2sIfVxS177rnnkJzO5iHyYGI6wN/+9rfu0ksvHRLKPlaucx8LprTwy7B5pgvc5ja36Vh6V1lllW6llVZazHJccW5/GgKzEIFGMGdhpTWVGwINgYbAdCPAuhaChBRlnfH4QPnAAhc9LG457rjjupe//OV1eyLDxtLvtdde3e6779496EEPGlr3EK7Z6pTJFAJnB4cowog/53oiqbQAiL8FPq6lDdF2De+Kc4kT/yqs/WkIzFIEGsGcpRXX1G4INAQaAtOJQEhUhnyRn5CqEKucxbngggu6I444ohJMZCnOgh8E06cis69lwmbjWdlYILNnp2vlgk+IIzwSXkDDOIeLgkwbEJcLsYS1IxbNRjBnY8toOk9EoBHMiYi0+4ZAQ6Ah0BAYWumQnUouC7Eyh5BDoBwcYoRkWexy5JFHdvvvv38lU4iURSwHHnhg9+hHP3q4QjrkqiaejX9YG8sRsh3LYzCJv6LlGkYIZ8oe7EJW+ceymevZCE3TuSHQR6ARzD4a7boh0BBoCDQEKgLID4LUJ0MsdAhnyFTIpS17Dj/88GrBNEwunn0ft9tuu27vvffuttpqq+qXtCFasxFqmFRSWZRHNGEQPJQP2eZch2A6K3P/EIcc6fs4kyVtcw2B2Y5AI5izvQab/g2BhkBDYBoQQHo4pAgRChnq79VonqW9Hy14Oeigg7qvfvWrdXGPVdP2fHzBC17Qbb311p0N1vvfOg9pnQa1p10kHEK0YeQaJiGF2R+TBTcOhuJxiec6GCOZ8JFmOLQuQnMNgVmMQCOYs7jymuoNgYZAQ2C6EAj5QY5ch2TGMsdSd/HFF3dnnHFG/e74qaee2p1//vk1rk3Ed9xxx+61r31td7vb3a7uf4muIpbkzXYHD4SQy/zLlCsWSWEhk8HSWXj8xYEJP4ukrCBvBBMqzd0SEGgE85ZQi60MDYGGQENghAiEEBGZa1+rsZAHkbSJOFLE7ze/+U13yimnVCsmssRa+chHPrJ7xA47dNuXI9ZKcm4pBBMusUhOJITKmaOWt8Q1lJ5vjcMtmJDDX3gsof0w4c01BGYrAo1gztaaa3o3BBoCDYFpQCDkqG+5RHouvPDC+nUei3a+853v1Jz5I1j5rrZ5l1tssUX31Kc+tQ6R8w9hCrFyH2vfNKjfRDYEGgIzBIFGMGdIRTQ1GgINgYbATEAAwTQc7swhg0jhH/7wh7qR+nvf+97u+9//fo2z6qqr1s8errPOOvWLPZtttlm37bbb1k8f3va2tx0WJ4QSyXQ90eo3jNguGgINgVsMAv8fAmD8rJjKhzsAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![Screen%20Shot%202021-07-18%20at%2020.39.59.png](attachment:Screen%20Shot%202021-07-18%20at%2020.39.59.png)\n",
    "\n",
    "Photo is cited by: https://www.google.com/url?sa=i&url=https%3A%2F%2Fmedium.com%2Fgeekculture%2Fridge-and-lasso-regression-51705b608fb9&psig=AOvVaw2KdB1GWJI9AWSkW5SyaXKy&ust=1626716220613000&source=images&cd=vfe&ved=0CAsQjRxqFwoTCJCY8YOV7fECFQAAAAAdAAAAABAD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For a real world example, we will work with **Hitters** dataset.\n",
    "\n",
    "It can be downloaded here: https://www.kaggle.com/floser/hitters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>446</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n",
       "0    293    66      1    30   29     14      1     293     66       1     30   \n",
       "1    315    81      7    24   38     39     14    3449    835      69    321   \n",
       "2    479   130     18    66   72     76      3    1624    457      63    224   \n",
       "3    496   141     20    65   78     37     11    5628   1575     225    828   \n",
       "4    321    87     10    39   42     30      2     396    101      12     48   \n",
       "\n",
       "   CRBI  CWalks League Division  PutOuts  Assists  Errors  Salary NewLeague  \n",
       "0    29      14      A        E      446       33      20     NaN         A  \n",
       "1   414     375      N        W      632       43      10   475.0         N  \n",
       "2   266     263      A        W      880       82      14   480.0         A  \n",
       "3   838     354      N        E      200       11       3   500.0         N  \n",
       "4    46      33      N        E      805       40       4    91.5         N  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts = pd.read_csv(\"Hitters.csv\")\n",
    "hts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will remove NA values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "hts.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>403.642586</td>\n",
       "      <td>147.307209</td>\n",
       "      <td>19.0</td>\n",
       "      <td>282.5</td>\n",
       "      <td>413.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>687.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>107.828897</td>\n",
       "      <td>45.125326</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>103.0</td>\n",
       "      <td>141.5</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>11.619772</td>\n",
       "      <td>8.757108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>263.0</td>\n",
       "      <td>54.745247</td>\n",
       "      <td>25.539816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>51.486692</td>\n",
       "      <td>25.882714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>41.114068</td>\n",
       "      <td>21.718056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years</th>\n",
       "      <td>263.0</td>\n",
       "      <td>7.311787</td>\n",
       "      <td>4.793616</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>2657.543726</td>\n",
       "      <td>2286.582929</td>\n",
       "      <td>19.0</td>\n",
       "      <td>842.5</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>3890.5</td>\n",
       "      <td>14053.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>722.186312</td>\n",
       "      <td>648.199644</td>\n",
       "      <td>4.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>4256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>69.239544</td>\n",
       "      <td>82.197581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>92.5</td>\n",
       "      <td>548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRuns</th>\n",
       "      <td>263.0</td>\n",
       "      <td>361.220532</td>\n",
       "      <td>331.198571</td>\n",
       "      <td>2.0</td>\n",
       "      <td>105.5</td>\n",
       "      <td>250.0</td>\n",
       "      <td>497.5</td>\n",
       "      <td>2165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>330.418251</td>\n",
       "      <td>323.367668</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>424.5</td>\n",
       "      <td>1659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CWalks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>260.266160</td>\n",
       "      <td>264.055868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>328.5</td>\n",
       "      <td>1566.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PutOuts</th>\n",
       "      <td>263.0</td>\n",
       "      <td>290.711027</td>\n",
       "      <td>279.934575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.5</td>\n",
       "      <td>224.0</td>\n",
       "      <td>322.5</td>\n",
       "      <td>1377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assists</th>\n",
       "      <td>263.0</td>\n",
       "      <td>118.760456</td>\n",
       "      <td>145.080577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Errors</th>\n",
       "      <td>263.0</td>\n",
       "      <td>8.593156</td>\n",
       "      <td>6.606574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>263.0</td>\n",
       "      <td>535.925882</td>\n",
       "      <td>451.118681</td>\n",
       "      <td>67.5</td>\n",
       "      <td>190.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2460.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std   min    25%     50%     75%      max\n",
       "AtBat    263.0   403.642586   147.307209  19.0  282.5   413.0   526.0    687.0\n",
       "Hits     263.0   107.828897    45.125326   1.0   71.5   103.0   141.5    238.0\n",
       "HmRun    263.0    11.619772     8.757108   0.0    5.0     9.0    18.0     40.0\n",
       "Runs     263.0    54.745247    25.539816   0.0   33.5    52.0    73.0    130.0\n",
       "RBI      263.0    51.486692    25.882714   0.0   30.0    47.0    71.0    121.0\n",
       "Walks    263.0    41.114068    21.718056   0.0   23.0    37.0    57.0    105.0\n",
       "Years    263.0     7.311787     4.793616   1.0    4.0     6.0    10.0     24.0\n",
       "CAtBat   263.0  2657.543726  2286.582929  19.0  842.5  1931.0  3890.5  14053.0\n",
       "CHits    263.0   722.186312   648.199644   4.0  212.0   516.0  1054.0   4256.0\n",
       "CHmRun   263.0    69.239544    82.197581   0.0   15.0    40.0    92.5    548.0\n",
       "CRuns    263.0   361.220532   331.198571   2.0  105.5   250.0   497.5   2165.0\n",
       "CRBI     263.0   330.418251   323.367668   3.0   95.0   230.0   424.5   1659.0\n",
       "CWalks   263.0   260.266160   264.055868   1.0   71.0   174.0   328.5   1566.0\n",
       "PutOuts  263.0   290.711027   279.934575   0.0  113.5   224.0   322.5   1377.0\n",
       "Assists  263.0   118.760456   145.080577   0.0    8.0    45.0   192.0    492.0\n",
       "Errors   263.0     8.593156     6.606574   0.0    3.0     7.0    13.0     32.0\n",
       "Salary   263.0   535.925882   451.118681  67.5  190.0   425.0   750.0   2460.0"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We will do **One Hot Encoding** to categorical columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>League_A</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_E</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_A</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   League_A  League_N  Division_E  Division_W  NewLeague_A  NewLeague_N\n",
       "1         0         1           0           1            0            1\n",
       "2         1         0           0           1            1            0\n",
       "3         0         1           1           0            0            1\n",
       "4         0         1           1           0            0            1\n",
       "5         1         0           0           1            1            0"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoded = pd.get_dummies(hts[[\"League\",\"Division\",\"NewLeague\"]])\n",
    "one_hot_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "new_hts = hts.drop([\"League\",\"Division\",\"NewLeague\",\"Salary\"],axis=1).astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat   Hits  HmRun  Runs   RBI  Walks  Years  CAtBat   CHits  CHmRun  \\\n",
       "1  315.0   81.0    7.0  24.0  38.0   39.0   14.0  3449.0   835.0    69.0   \n",
       "2  479.0  130.0   18.0  66.0  72.0   76.0    3.0  1624.0   457.0    63.0   \n",
       "3  496.0  141.0   20.0  65.0  78.0   37.0   11.0  5628.0  1575.0   225.0   \n",
       "4  321.0   87.0   10.0  39.0  42.0   30.0    2.0   396.0   101.0    12.0   \n",
       "5  594.0  169.0    4.0  74.0  51.0   35.0   11.0  4408.0  1133.0    19.0   \n",
       "\n",
       "   CRuns   CRBI  CWalks  PutOuts  Assists  Errors  League_N  Division_W  \\\n",
       "1  321.0  414.0   375.0    632.0     43.0    10.0         1           1   \n",
       "2  224.0  266.0   263.0    880.0     82.0    14.0         0           1   \n",
       "3  828.0  838.0   354.0    200.0     11.0     3.0         1           0   \n",
       "4   48.0   46.0    33.0    805.0     40.0     4.0         1           0   \n",
       "5  501.0  336.0   194.0    282.0    421.0    25.0         0           1   \n",
       "\n",
       "   NewLeague_N  \n",
       "1            1  \n",
       "2            0  \n",
       "3            1  \n",
       "4            1  \n",
       "5            0  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.concat([new_hts,one_hot_encoded[[\"League_N\",\"Division_W\",\"NewLeague_N\"]]],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y = hts.Salary # Target-dependent variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will split our dataset as train and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 20)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 19)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Independent Variables\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263,)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dependent Variables\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape:  (210, 19)\n",
      "X_test Shape:  (53, 19)\n",
      "y_train Shape:  (210,)\n",
      "y_test Shape:  (53,)\n"
     ]
    }
   ],
   "source": [
    "X_train = X.iloc[:210]\n",
    "X_test = X.iloc[210:]\n",
    "y_train = y[:210]\n",
    "y_test = y[210:]\n",
    "\n",
    "print(\"X_train Shape: \",X_train.shape)\n",
    "print(\"X_test Shape: \",X_test.shape)\n",
    "print(\"y_train Shape: \",y_train.shape)\n",
    "print(\"y_test Shape: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654454.536722869, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "lasso_model = Lasso(alpha=0.1).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.1)"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -2.36728845,    8.69562348,    2.6253349 ,   -1.2995984 ,\n",
       "         -1.01285484,    5.51874641,    1.35831429,   -0.18352931,\n",
       "         -0.16612724,   -0.72026897,    1.80604429,    1.20306017,\n",
       "         -0.84327277,    0.41573214,    0.64198626,   -2.94444835,\n",
       "         19.81585838, -128.16251762,   49.86209437])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4741.4905441477895, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5852.535247590393, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5668.0616027750075, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5422.723332680762, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5178.819077331573, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5018.75422764197, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5995.012613646686, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17837.73162775114, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10260.940729282796, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4948.56518740207, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5655.426647402346, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6431.015314571559, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7300.551545508206, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8288.957691770047, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9425.132725425065, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12128.612841911614, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18382.783947553486, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23527.199944138527, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 31174.47835677117, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124568.37808854505, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49011.954543955624, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81749.26690491661, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2812168.6291002017, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4979999.336325005, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6147221.338153484, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6921220.31832012, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7484585.210140525, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7908565.351475533, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8243356.804016301, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8512319.305686789, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8726125.050835155, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8882989.324428681, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9038364.724415025, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9157769.982291875, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9244302.50778126, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9314333.501264011, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9372549.641845852, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9422091.613394849, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9465543.816327574, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9504441.357134392, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9539098.266122907, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9567901.723145576, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9582929.020821929, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9593318.72284683, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9602038.763692817, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9609402.816254433, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9615639.500357235, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9620940.897624116, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9625465.009534193, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9629337.636631094, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9632662.129321126, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9635529.533385454, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9638007.57985859, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9640155.6096634, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9642022.102850894, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9643648.392476393, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9645069.38774582, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9646312.752961509, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9647403.855186518, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9648362.51889733, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9649206.922588188, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9649951.535139479, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9650609.444491453, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9651191.464208562, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9651707.00792479, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9652164.180647898, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9652570.181759909, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9652931.022889819, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9653252.023369364, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9653537.831711646, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9653792.46692801, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654019.55153346, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654222.163395911, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654403.043929426, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654564.608082337, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654708.986698436, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654838.062789451, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654953.502686488, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655056.782872783, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655149.21358974, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655231.957723562, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655306.04909569, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655372.407299647, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655431.852282163, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655485.111239852, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655532.841599336, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655575.620959532, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655613.968085242, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655648.346382882, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655679.16999906, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655706.809211519, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655731.595200952, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655753.824280152, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655773.761646599, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655791.644715833, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655807.686084785, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655822.076169565, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655834.98555393, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655846.567082979, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655856.95773049, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655866.280264769, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9655874.644736934, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "lambda_values= 10**np.linspace(5,-2,150)*0.5\n",
    "lasso_model = Lasso()\n",
    "coefficients = []\n",
    "\n",
    "for lam in lambda_values:\n",
    "    lasso_model.set_params(alpha=lam)\n",
    "    lasso_model.fit(X_train,y_train)\n",
    "    coefficients.append(lasso_model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.plot(lambda_values*2,coefficients)\n",
    "ax.set_xscale(\"log\")\n",
    "\n",
    "plt.axis(\"tight\")\n",
    "plt.xlabel(\"Lambda Values - Alpha\")\n",
    "plt.ylabel(\"Coefficients - Weights\")\n",
    "plt.title(\"Lasso Coefficients\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.005)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 400.7827288 ,  756.98597514, 1176.65551163,  649.19511665,\n",
       "        570.48964377,  193.87333341,  107.02884517,  194.67623188,\n",
       "        655.19484968,  971.39228125])"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model.predict(X_train)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=lasso_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303.251388887823"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Error\n",
    "np.sqrt(mean_squared_error(y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5751635199453824"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=lasso_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "336.6329836759592"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Error\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22412716410250244"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "Lasso_cv = LassoCV(alphas=None,\n",
    "                   cv=15,\n",
    "                   max_iter=15000,\n",
    "                   normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LassoCV(cv=15, max_iter=15000, normalize=True)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lasso_cv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07340278835886885"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lasso_cv.alpha_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will create our last model with optimal alpha value that seems 0.07340278835886885."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654858.0141913, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "lasso_tuned = Lasso(alpha=Lasso_cv.alpha_).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=lasso_tuned.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303.2514234667017"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Error\n",
    "np.sqrt(mean_squared_error(y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.575163423059627"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=lasso_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "336.5698762152672"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Error\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22441803741182342"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Elastic Net Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Elastic-Net Regression is combines Lasso Regression with Ridge Regression to give you the best of both worlds. It works well when there are lots of useless variables that need to be removed from the equation and it works well when there are lots of useful variables that need to be retained. And it does better than either one when it comes to handling correlated variables. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " Elastic Net aims at minimizing the following loss function:"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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Q60c6lvveRgDHJpjJeVnpMyEJSOTHQKe621Jd4w4IaLzlG6ttEokdIVba6itbfFcnZ3t11xyI35vslXV2uyvimWFwNsYm12wUnDcVAU2+GIB0/KIAFT9zVx7tnO6NQOYbKy2Oizl8cPRSgC7vr0iKM3A2nnUUTa51bFwyAQFNvkieUayCFPWUlGdyFSL3vG/5xgqr5Vuz7yt0fQWZs3GUvFewfUtHk+sWQi4/hED5Tzvib6n6nYYJpaSAG/NU5vu9EGj5xr0stTVPRMDk+sRRP8nm7AtKAqqSfmvjGFgJ8oVYRcKxvur4/voIbPnG61toC56OgMn16R6wyH6RZPablrqkDCKNH5ZwfEweO1jI96yPXKST7+sR6PGN9Vq4ByOwFgGT61p8HysdgswuCFOJ8vhOvo6FId6yTO18f20EMr/IfOEMK/X3zkf60kdYI22eUtd4fhlpk+sXLPxkBIzAAxEY+aMNHGfHv5H+QLg2TTae/0Fkct10FVcwAkbACLxHgJ220zwE7oinyXWef1iSETACL4KAjnb3BvW97V4EnmE1jefXkJlcv8bEOUbACNwcAf2eL5LkKDP7+E558Q+gAI3a3RymbvOM59dQmVy/xsQ5RsAIPAABfbWMqSbX4wNuPN9jaHJ9j4ffjIAReAgC7ErLHWmv6d65fo2U8XyPicn1PR5+MwJG4CEIQJAjX7YCi3ZntOXi3ek/BIzne08wub7Hw29GwAjcHAF+H9QHODc39RTzjGcOs8k1x8W5RsAI3BQB7Tqf+j/ZzB5W45kjanLNcXGuETACRsAIGIHdCJhcd0PnhkbACBgBI2AEcgRMrjkuzjUCRsAIGAEjsBsBk+tu6NzQCBgBI2AEjECOgMk1x8W5RsAIGAEjYAR2I2By3Q2dGxoBI2AEjIARyBH4F9ovnpwnf448AAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    "Photo is cited by: https://www.datacamp.com/community/tutorials/tutorial-ridge-lasso-elastic-net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For a real world example, we will work with **Hitters** dataset.\n",
    "\n",
    "It can be downloaded here: https://www.kaggle.com/floser/hitters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>293</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>446</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n",
       "0    293    66      1    30   29     14      1     293     66       1     30   \n",
       "1    315    81      7    24   38     39     14    3449    835      69    321   \n",
       "2    479   130     18    66   72     76      3    1624    457      63    224   \n",
       "3    496   141     20    65   78     37     11    5628   1575     225    828   \n",
       "4    321    87     10    39   42     30      2     396    101      12     48   \n",
       "\n",
       "   CRBI  CWalks League Division  PutOuts  Assists  Errors  Salary NewLeague  \n",
       "0    29      14      A        E      446       33      20     NaN         A  \n",
       "1   414     375      N        W      632       43      10   475.0         N  \n",
       "2   266     263      A        W      880       82      14   480.0         A  \n",
       "3   838     354      N        E      200       11       3   500.0         N  \n",
       "4    46      33      N        E      805       40       4    91.5         N  "
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts = pd.read_csv(\"Hitters.csv\")\n",
    "hts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will remove NA values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "hts.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>403.642586</td>\n",
       "      <td>147.307209</td>\n",
       "      <td>19.0</td>\n",
       "      <td>282.5</td>\n",
       "      <td>413.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>687.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>107.828897</td>\n",
       "      <td>45.125326</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>103.0</td>\n",
       "      <td>141.5</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>11.619772</td>\n",
       "      <td>8.757108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>263.0</td>\n",
       "      <td>54.745247</td>\n",
       "      <td>25.539816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>51.486692</td>\n",
       "      <td>25.882714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>41.114068</td>\n",
       "      <td>21.718056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years</th>\n",
       "      <td>263.0</td>\n",
       "      <td>7.311787</td>\n",
       "      <td>4.793616</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAtBat</th>\n",
       "      <td>263.0</td>\n",
       "      <td>2657.543726</td>\n",
       "      <td>2286.582929</td>\n",
       "      <td>19.0</td>\n",
       "      <td>842.5</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>3890.5</td>\n",
       "      <td>14053.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHits</th>\n",
       "      <td>263.0</td>\n",
       "      <td>722.186312</td>\n",
       "      <td>648.199644</td>\n",
       "      <td>4.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>4256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHmRun</th>\n",
       "      <td>263.0</td>\n",
       "      <td>69.239544</td>\n",
       "      <td>82.197581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>92.5</td>\n",
       "      <td>548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRuns</th>\n",
       "      <td>263.0</td>\n",
       "      <td>361.220532</td>\n",
       "      <td>331.198571</td>\n",
       "      <td>2.0</td>\n",
       "      <td>105.5</td>\n",
       "      <td>250.0</td>\n",
       "      <td>497.5</td>\n",
       "      <td>2165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRBI</th>\n",
       "      <td>263.0</td>\n",
       "      <td>330.418251</td>\n",
       "      <td>323.367668</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>424.5</td>\n",
       "      <td>1659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CWalks</th>\n",
       "      <td>263.0</td>\n",
       "      <td>260.266160</td>\n",
       "      <td>264.055868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>328.5</td>\n",
       "      <td>1566.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PutOuts</th>\n",
       "      <td>263.0</td>\n",
       "      <td>290.711027</td>\n",
       "      <td>279.934575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.5</td>\n",
       "      <td>224.0</td>\n",
       "      <td>322.5</td>\n",
       "      <td>1377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assists</th>\n",
       "      <td>263.0</td>\n",
       "      <td>118.760456</td>\n",
       "      <td>145.080577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Errors</th>\n",
       "      <td>263.0</td>\n",
       "      <td>8.593156</td>\n",
       "      <td>6.606574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>263.0</td>\n",
       "      <td>535.925882</td>\n",
       "      <td>451.118681</td>\n",
       "      <td>67.5</td>\n",
       "      <td>190.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2460.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std   min    25%     50%     75%      max\n",
       "AtBat    263.0   403.642586   147.307209  19.0  282.5   413.0   526.0    687.0\n",
       "Hits     263.0   107.828897    45.125326   1.0   71.5   103.0   141.5    238.0\n",
       "HmRun    263.0    11.619772     8.757108   0.0    5.0     9.0    18.0     40.0\n",
       "Runs     263.0    54.745247    25.539816   0.0   33.5    52.0    73.0    130.0\n",
       "RBI      263.0    51.486692    25.882714   0.0   30.0    47.0    71.0    121.0\n",
       "Walks    263.0    41.114068    21.718056   0.0   23.0    37.0    57.0    105.0\n",
       "Years    263.0     7.311787     4.793616   1.0    4.0     6.0    10.0     24.0\n",
       "CAtBat   263.0  2657.543726  2286.582929  19.0  842.5  1931.0  3890.5  14053.0\n",
       "CHits    263.0   722.186312   648.199644   4.0  212.0   516.0  1054.0   4256.0\n",
       "CHmRun   263.0    69.239544    82.197581   0.0   15.0    40.0    92.5    548.0\n",
       "CRuns    263.0   361.220532   331.198571   2.0  105.5   250.0   497.5   2165.0\n",
       "CRBI     263.0   330.418251   323.367668   3.0   95.0   230.0   424.5   1659.0\n",
       "CWalks   263.0   260.266160   264.055868   1.0   71.0   174.0   328.5   1566.0\n",
       "PutOuts  263.0   290.711027   279.934575   0.0  113.5   224.0   322.5   1377.0\n",
       "Assists  263.0   118.760456   145.080577   0.0    8.0    45.0   192.0    492.0\n",
       "Errors   263.0     8.593156     6.606574   0.0    3.0     7.0    13.0     32.0\n",
       "Salary   263.0   535.925882   451.118681  67.5  190.0   425.0   750.0   2460.0"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We will do **One Hot Encoding** to categorical columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>League_A</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_E</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_A</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   League_A  League_N  Division_E  Division_W  NewLeague_A  NewLeague_N\n",
       "1         0         1           0           1            0            1\n",
       "2         1         0           0           1            1            0\n",
       "3         0         1           1           0            0            1\n",
       "4         0         1           1           0            0            1\n",
       "5         1         0           0           1            1            0"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoded = pd.get_dummies(hts[[\"League\",\"Division\",\"NewLeague\"]])\n",
    "one_hot_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "new_hts = hts.drop([\"League\",\"Division\",\"NewLeague\",\"Salary\"],axis=1).astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat   Hits  HmRun  Runs   RBI  Walks  Years  CAtBat   CHits  CHmRun  \\\n",
       "1  315.0   81.0    7.0  24.0  38.0   39.0   14.0  3449.0   835.0    69.0   \n",
       "2  479.0  130.0   18.0  66.0  72.0   76.0    3.0  1624.0   457.0    63.0   \n",
       "3  496.0  141.0   20.0  65.0  78.0   37.0   11.0  5628.0  1575.0   225.0   \n",
       "4  321.0   87.0   10.0  39.0  42.0   30.0    2.0   396.0   101.0    12.0   \n",
       "5  594.0  169.0    4.0  74.0  51.0   35.0   11.0  4408.0  1133.0    19.0   \n",
       "\n",
       "   CRuns   CRBI  CWalks  PutOuts  Assists  Errors  League_N  Division_W  \\\n",
       "1  321.0  414.0   375.0    632.0     43.0    10.0         1           1   \n",
       "2  224.0  266.0   263.0    880.0     82.0    14.0         0           1   \n",
       "3  828.0  838.0   354.0    200.0     11.0     3.0         1           0   \n",
       "4   48.0   46.0    33.0    805.0     40.0     4.0         1           0   \n",
       "5  501.0  336.0   194.0    282.0    421.0    25.0         0           1   \n",
       "\n",
       "   NewLeague_N  \n",
       "1            1  \n",
       "2            0  \n",
       "3            1  \n",
       "4            1  \n",
       "5            0  "
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.concat([new_hts,one_hot_encoded[[\"League_N\",\"Division_W\",\"NewLeague_N\"]]],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y = hts.Salary # Target-dependent variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will split our dataset as train and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 20)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 19)"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Independent Variables\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263,)"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dependent Variables\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape:  (210, 19)\n",
      "X_test Shape:  (53, 19)\n",
      "y_train Shape:  (210,)\n",
      "y_test Shape:  (53,)\n"
     ]
    }
   ],
   "source": [
    "X_train = X.iloc[:210]\n",
    "X_test = X.iloc[210:]\n",
    "y_train = y[:210]\n",
    "y_test = y[210:]\n",
    "\n",
    "print(\"X_train Shape: \",X_train.shape)\n",
    "print(\"X_test Shape: \",X_test.shape)\n",
    "print(\"y_train Shape: \",y_train.shape)\n",
    "print(\"y_test Shape: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9964166.327909471, tolerance: 4545.724279285033\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "elastic_net_model = ElasticNet().fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNet()"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_net_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -2.57038767,   9.08966777,   1.49845497,  -1.13947762,\n",
       "        -0.34885378,   5.42445699,   1.6561179 ,  -0.18568568,\n",
       "        -0.15639425,  -0.61738565,   1.84946479,   1.10766503,\n",
       "        -0.8168196 ,   0.43065118,   0.63692288,  -2.84916177,\n",
       "        13.1999717 , -39.32827371,  14.65618016])"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_net_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.1698547030835"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_net_model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNet()"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_net_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 420.3065126 ,  825.64586434, 1104.11471575,  589.46201287,\n",
       "        630.36531273,  153.49242813,  164.95214949,  208.627058  ,\n",
       "        620.66917997,  949.44403372])"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_net_model.predict(X_train)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=elastic_net_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "306.74633648485917"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Error\n",
    "np.sqrt(mean_squared_error(y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5653146797994274"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=elastic_net_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "330.1719387567114"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Error\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25362422683742336"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "elastic_net_cv = ElasticNetCV(cv=15,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNetCV(cv=15, random_state=42)"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_net_cv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1116.4729085556469"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_net_cv.alpha_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we will create our last model with optimal alpha value that seems 1116.4729085556469."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "elastic_net_tuned = ElasticNet(alpha=elastic_net_cv.alpha_).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=elastic_net_tuned.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "326.9160736762325"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Error\n",
    "np.sqrt(mean_squared_error(y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50627086776619"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred=elastic_net_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "328.92750084588874"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Error\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25923989439099737"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "| Model | Train Error | Test Error |\n",
    "| --- | --- | --- |\n",
    "| Partial Least Squares(PLS) | 309.05 | 330.64 |\n",
    "| Ridge Regression(L2 Regularization) | 306.60 | 327.87 |\n",
    "| Lasso Regression(L1 Regularization) | 303.25 | 336.56 |\n",
    "| Elastic Net Regression | 326.91 | 328.92 |\n"
   ]
  }
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