2018/day32_energy_score_Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict a building's Energy Star \n",
    "### Part 2- Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports \n",
    "\n",
    "We will use the standard data science and machine learning libraries in this project. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas and numpy for data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# No warnings about setting value on copy of slice\n",
    "pd.options.mode.chained_assignment = None\n",
    "pd.set_option('display.max_columns', 60)\n",
    "\n",
    "# Matplotlib for visualization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Set default font size\n",
    "plt.rcParams['font.size'] = 24\n",
    "\n",
    "from IPython.core.pylabtools import figsize\n",
    "\n",
    "# Seaborn for visualization\n",
    "import seaborn as sns\n",
    "sns.set(font_scale = 2)\n",
    "\n",
    "# Imputing missing values and scaling values\n",
    "from sklearn.preprocessing import Imputer, MinMaxScaler\n",
    "\n",
    "# Machine Learning Models\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# Hyperparameter tuning\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in Data\n",
    "\n",
    "First let's read in the formatted data from the previous notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Feature Size:  (6622, 64)\n",
      "Testing Feature Size:   (2839, 64)\n",
      "Training Labels Size:   (6622, 1)\n",
      "Testing Labels Size:    (2839, 1)\n"
     ]
    }
   ],
   "source": [
    "# Read in data into dataframes \n",
    "train_features = pd.read_csv('data/training_features.csv')\n",
    "test_features = pd.read_csv('data/testing_features.csv')\n",
    "train_labels = pd.read_csv('data/training_labels.csv')\n",
    "test_labels = pd.read_csv('data/testing_labels.csv')\n",
    "\n",
    "# Display sizes of data\n",
    "print('Training Feature Size: ', train_features.shape)\n",
    "print('Testing Feature Size:  ', test_features.shape)\n",
    "print('Training Labels Size:  ', train_labels.shape)\n",
    "print('Testing Labels Size:   ', test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a reminder, here is what the formatted data looks like. In the first notebook, we engineered a number features by taking the natural log of the variables, included two categorical variables, and selected a subset of features by removing highly collinear features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Property Id</th>\n",
       "      <th>DOF Gross Floor Area</th>\n",
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       "      <th>Occupancy</th>\n",
       "      <th>Site EUI (kBtu/ft²)</th>\n",
       "      <th>Weather Normalized Site Electricity Intensity (kWh/ft²)</th>\n",
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       "      <th>Longitude</th>\n",
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       "      <th>Census Tract</th>\n",
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       "      <th>log_Water Intensity (All Water Sources) (gal/ft²)</th>\n",
       "      <th>Borough_Staten Island</th>\n",
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       "      <th>Largest Property Use Type_Multifamily Housing</th>\n",
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       "      <th>Largest Property Use Type_Restaurant</th>\n",
       "      <th>Largest Property Use Type_Retail Store</th>\n",
       "      <th>Largest Property Use Type_Self-Storage Facility</th>\n",
       "      <th>Largest Property Use Type_Senior Care Community</th>\n",
       "      <th>Largest Property Use Type_Social/Meeting Hall</th>\n",
       "      <th>Largest Property Use Type_Strip Mall</th>\n",
       "      <th>Largest Property Use Type_Supermarket/Grocery Store</th>\n",
       "      <th>Largest Property Use Type_Urgent Care/Clinic/Other Outpatient</th>\n",
       "      <th>Largest Property Use Type_Wholesale Club/Supercenter</th>\n",
       "      <th>Largest Property Use Type_Worship Facility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>90300.0</td>\n",
       "      <td>1950</td>\n",
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       "      <td>100</td>\n",
       "      <td>126.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>99.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>52000.0</td>\n",
       "      <td>1926</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>95.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.835496</td>\n",
       "      <td>-73.887745</td>\n",
       "      <td>3.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>5.384036</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>104700.0</td>\n",
       "      <td>1954</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>40.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.663206</td>\n",
       "      <td>-73.949469</td>\n",
       "      <td>9.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5.017280</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14774</td>\n",
       "      <td>3393340</td>\n",
       "      <td>129333.0</td>\n",
       "      <td>1992</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>157.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.622968</td>\n",
       "      <td>-74.078742</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.510853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2704325</td>\n",
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       "      <td>1927</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>62.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.65</td>\n",
       "      <td>40.782421</td>\n",
       "      <td>-73.972622</td>\n",
       "      <td>7.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>6.123589</td>\n",
       "      <td>3.355153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>182655.0</td>\n",
       "      <td>1929</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>52.9</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.80</td>\n",
       "      <td>40.725136</td>\n",
       "      <td>-74.004438</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>5.516649</td>\n",
       "      <td>1.568616</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10846</td>\n",
       "      <td>5737475</td>\n",
       "      <td>65400.0</td>\n",
       "      <td>1942</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>66.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.14</td>\n",
       "      <td>40.637833</td>\n",
       "      <td>-73.973045</td>\n",
       "      <td>12.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>5.426271</td>\n",
       "      <td>4.206780</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4280</td>\n",
       "      <td>2670505</td>\n",
       "      <td>113150.0</td>\n",
       "      <td>1938</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>78.4</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.73</td>\n",
       "      <td>40.776035</td>\n",
       "      <td>-73.964418</td>\n",
       "      <td>8.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>6.067036</td>\n",
       "      <td>3.425239</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12974</td>\n",
       "      <td>2964670</td>\n",
       "      <td>137700.0</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>41.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.170447</td>\n",
       "      <td>3.736717</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13244</td>\n",
       "      <td>4414693</td>\n",
       "      <td>63693.0</td>\n",
       "      <td>1941</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>97.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>86.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.680855</td>\n",
       "      <td>4.464528</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3903</td>\n",
       "      <td>2669664</td>\n",
       "      <td>82644.0</td>\n",
       "      <td>1922</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>55.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762510</td>\n",
       "      <td>-73.970085</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11203.0</td>\n",
       "      <td>1.335001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8336</td>\n",
       "      <td>2809354</td>\n",
       "      <td>51317.0</td>\n",
       "      <td>1925</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>118.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.849372</td>\n",
       "      <td>-73.832075</td>\n",
       "      <td>10.0</td>\n",
       "      <td>26601.0</td>\n",
       "      <td>5.662960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Order  Property Id  DOF Gross Floor Area  Year Built  \\\n",
       "0   13276      5849784               90300.0        1950   \n",
       "1    7377      4398442               52000.0        1926   \n",
       "2    9479      4665374              104700.0        1954   \n",
       "3   14774      3393340              129333.0        1992   \n",
       "4    3286      2704325              109896.0        1927   \n",
       "5    1060      2430725              182655.0        1929   \n",
       "6   10846      5737475               65400.0        1942   \n",
       "7    4280      2670505              113150.0        1938   \n",
       "8   12974      2964670              137700.0        1959   \n",
       "9   13244      4414693               63693.0        1941   \n",
       "10   3903      2669664               82644.0        1922   \n",
       "11   8336      2809354               51317.0        1925   \n",
       "\n",
       "    Number of Buildings - Self-reported  Occupancy  Site EUI (kBtu/ft²)  \\\n",
       "0                                     1        100                126.0   \n",
       "1                                     1        100                 95.4   \n",
       "2                                     1        100                 40.4   \n",
       "3                                     1        100                157.1   \n",
       "4                                     1        100                 62.3   \n",
       "5                                     1         90                 52.9   \n",
       "6                                     1        100                 66.8   \n",
       "7                                     1        100                 78.4   \n",
       "8                                     1        100                 63.0   \n",
       "9                                     1        100                 97.8   \n",
       "10                                    1        100                 55.4   \n",
       "11                                    1        100                118.7   \n",
       "\n",
       "    Weather Normalized Site Electricity Intensity (kWh/ft²)  \\\n",
       "0                                                 5.2         \n",
       "1                                                 4.7         \n",
       "2                                                 3.8         \n",
       "3                                                16.9         \n",
       "4                                                 3.5         \n",
       "5                                                 9.7         \n",
       "6                                                 3.0         \n",
       "7                                                 5.7         \n",
       "8                                                 3.4         \n",
       "9                                                 4.3         \n",
       "10                                                4.5         \n",
       "11                                                3.6         \n",
       "\n",
       "    Weather Normalized Site Natural Gas Intensity (therms/ft²)  \\\n",
       "0                                                 1.2            \n",
       "1                                                 0.9            \n",
       "2                                                 0.3            \n",
       "3                                                 1.1            \n",
       "4                                                 0.0            \n",
       "5                                                 0.2            \n",
       "6                                                 0.6            \n",
       "7                                                 NaN            \n",
       "8                                                 0.5            \n",
       "9                                                 0.8            \n",
       "10                                                0.0            \n",
       "11                                                1.1            \n",
       "\n",
       "    Water Intensity (All Water Sources) (gal/ft²)   Latitude  Longitude  \\\n",
       "0                                           99.41        NaN        NaN   \n",
       "1                                             NaN  40.835496 -73.887745   \n",
       "2                                             NaN  40.663206 -73.949469   \n",
       "3                                             NaN  40.622968 -74.078742   \n",
       "4                                           28.65  40.782421 -73.972622   \n",
       "5                                            4.80  40.725136 -74.004438   \n",
       "6                                           67.14  40.637833 -73.973045   \n",
       "7                                           30.73  40.776035 -73.964418   \n",
       "8                                           41.96        NaN        NaN   \n",
       "9                                           86.88        NaN        NaN   \n",
       "10                                            NaN  40.762510 -73.970085   \n",
       "11                                            NaN  40.849372 -73.832075   \n",
       "\n",
       "    Community Board  Census Tract  \\\n",
       "0               NaN           NaN   \n",
       "1               3.0         161.0   \n",
       "2               9.0         329.0   \n",
       "3               1.0          27.0   \n",
       "4               7.0         165.0   \n",
       "5               2.0          37.0   \n",
       "6              12.0         490.0   \n",
       "7               8.0         142.0   \n",
       "8               NaN           NaN   \n",
       "9               NaN           NaN   \n",
       "10              5.0       11203.0   \n",
       "11             10.0       26601.0   \n",
       "\n",
       "    log_Direct GHG Emissions (Metric Tons CO2e)  \\\n",
       "0                                      6.088818   \n",
       "1                                      5.384036   \n",
       "2                                      5.017280   \n",
       "3                                      6.510853   \n",
       "4                                      6.123589   \n",
       "5                                      5.516649   \n",
       "6                                      5.426271   \n",
       "7                                      6.067036   \n",
       "8                                      6.170447   \n",
       "9                                      5.680855   \n",
       "10                                     1.335001   \n",
       "11                                     5.662960   \n",
       "\n",
       "    log_Water Intensity (All Water Sources) (gal/ft²)  Borough_Staten Island  \\\n",
       "0                                            4.599253                      0   \n",
       "1                                                 NaN                      0   \n",
       "2                                                 NaN                      0   \n",
       "3                                                 NaN                      1   \n",
       "4                                            3.355153                      0   \n",
       "5                                            1.568616                      0   \n",
       "6                                            4.206780                      0   \n",
       "7                                            3.425239                      0   \n",
       "8                                            3.736717                      0   \n",
       "9                                            4.464528                      0   \n",
       "10                                                NaN                      0   \n",
       "11                                                NaN                      0   \n",
       "\n",
       "    Largest Property Use Type_Adult Education  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "2                                           0   \n",
       "3                                           0   \n",
       "4                                           0   \n",
       "5                                           0   \n",
       "6                                           0   \n",
       "7                                           0   \n",
       "8                                           0   \n",
       "9                                           0   \n",
       "10                                          0   \n",
       "11                                          0   \n",
       "\n",
       "    Largest Property Use Type_Automobile Dealership  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "5                                                 0   \n",
       "6                                                 0   \n",
       "7                                                 0   \n",
       "8                                                 0   \n",
       "9                                                 0   \n",
       "10                                                0   \n",
       "11                                                0   \n",
       "\n",
       "    Largest Property Use Type_Bank Branch  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "5                                       0   \n",
       "6                                       0   \n",
       "7                                       0   \n",
       "8                                       0   \n",
       "9                                       0   \n",
       "10                                      0   \n",
       "11                                      0   \n",
       "\n",
       "    Largest Property Use Type_College/University  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "5                                              0   \n",
       "6                                              0   \n",
       "7                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "10                                             0   \n",
       "11                                             0   \n",
       "\n",
       "    Largest Property Use Type_Convenience Store without Gas Station  \\\n",
       "0                                                   0                 \n",
       "1                                                   0                 \n",
       "2                                                   0                 \n",
       "3                                                   0                 \n",
       "4                                                   0                 \n",
       "5                                                   0                 \n",
       "6                                                   0                 \n",
       "7                                                   0                 \n",
       "8                                                   0                 \n",
       "9                                                   0                 \n",
       "10                                                  0                 \n",
       "11                                                  0                 \n",
       "\n",
       "    Largest Property Use Type_Courthouse  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "5                                      0   \n",
       "6                                      0   \n",
       "7                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "10                                     0   \n",
       "11                                     0   \n",
       "\n",
       "    Largest Property Use Type_Distribution Center  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "5                                               0   \n",
       "6                                               0   \n",
       "7                                               0   \n",
       "8                                               0   \n",
       "9                                               0   \n",
       "10                                              0   \n",
       "11                                              0   \n",
       "\n",
       "    Largest Property Use Type_Enclosed Mall  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "5                                         0   \n",
       "6                                         0   \n",
       "7                                         0   \n",
       "8                                         0   \n",
       "9                                         0   \n",
       "10                                        0   \n",
       "11                                        0   \n",
       "\n",
       "    Largest Property Use Type_Financial Office  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "5                                            0   \n",
       "6                                            0   \n",
       "7                                            0   \n",
       "8                                            0   \n",
       "9                                            0   \n",
       "10                                           0   \n",
       "11                                           0   \n",
       "\n",
       "    Largest Property Use Type_Hospital (General Medical & Surgical)  \\\n",
       "0                                                   0                 \n",
       "1                                                   0                 \n",
       "2                                                   0                 \n",
       "3                                                   0                 \n",
       "4                                                   0                 \n",
       "5                                                   0                 \n",
       "6                                                   0                 \n",
       "7                                                   0                 \n",
       "8                                                   0                 \n",
       "9                                                   0                 \n",
       "10                                                  0                 \n",
       "11                                                  0                 \n",
       "\n",
       "    Largest Property Use Type_Hotel  Largest Property Use Type_K-12 School  \\\n",
       "0                                 0                                      0   \n",
       "1                                 0                                      0   \n",
       "2                                 0                                      0   \n",
       "3                                 0                                      0   \n",
       "4                                 0                                      0   \n",
       "5                                 0                                      0   \n",
       "6                                 0                                      0   \n",
       "7                                 0                                      0   \n",
       "8                                 0                                      0   \n",
       "9                                 0                                      0   \n",
       "10                                0                                      0   \n",
       "11                                0                                      0   \n",
       "\n",
       "    Largest Property Use Type_Library  \\\n",
       "0                                   0   \n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "5                                   0   \n",
       "6                                   0   \n",
       "7                                   0   \n",
       "8                                   0   \n",
       "9                                   0   \n",
       "10                                  0   \n",
       "11                                  0   \n",
       "\n",
       "                       ...                      \\\n",
       "0                      ...                       \n",
       "1                      ...                       \n",
       "2                      ...                       \n",
       "3                      ...                       \n",
       "4                      ...                       \n",
       "5                      ...                       \n",
       "6                      ...                       \n",
       "7                      ...                       \n",
       "8                      ...                       \n",
       "9                      ...                       \n",
       "10                     ...                       \n",
       "11                     ...                       \n",
       "\n",
       "    Largest Property Use Type_Multifamily Housing  \\\n",
       "0                                               1   \n",
       "1                                               1   \n",
       "2                                               1   \n",
       "3                                               0   \n",
       "4                                               1   \n",
       "5                                               0   \n",
       "6                                               1   \n",
       "7                                               1   \n",
       "8                                               1   \n",
       "9                                               1   \n",
       "10                                              1   \n",
       "11                                              1   \n",
       "\n",
       "    Largest Property Use Type_Museum  \\\n",
       "0                                  0   \n",
       "1                                  0   \n",
       "2                                  0   \n",
       "3                                  0   \n",
       "4                                  0   \n",
       "5                                  0   \n",
       "6                                  0   \n",
       "7                                  0   \n",
       "8                                  0   \n",
       "9                                  0   \n",
       "10                                 0   \n",
       "11                                 0   \n",
       "\n",
       "    Largest Property Use Type_Non-Refrigerated Warehouse  \\\n",
       "0                                                   0      \n",
       "1                                                   0      \n",
       "2                                                   0      \n",
       "3                                                   0      \n",
       "4                                                   0      \n",
       "5                                                   0      \n",
       "6                                                   0      \n",
       "7                                                   0      \n",
       "8                                                   0      \n",
       "9                                                   0      \n",
       "10                                                  0      \n",
       "11                                                  0      \n",
       "\n",
       "    Largest Property Use Type_Other  \\\n",
       "0                                 0   \n",
       "1                                 0   \n",
       "2                                 0   \n",
       "3                                 0   \n",
       "4                                 0   \n",
       "5                                 0   \n",
       "6                                 0   \n",
       "7                                 0   \n",
       "8                                 0   \n",
       "9                                 0   \n",
       "10                                0   \n",
       "11                                0   \n",
       "\n",
       "    Largest Property Use Type_Other - Education  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "5                                             0   \n",
       "6                                             0   \n",
       "7                                             0   \n",
       "8                                             0   \n",
       "9                                             0   \n",
       "10                                            0   \n",
       "11                                            0   \n",
       "\n",
       "    Largest Property Use Type_Other - Entertainment/Public Assembly  \\\n",
       "0                                                   0                 \n",
       "1                                                   0                 \n",
       "2                                                   0                 \n",
       "3                                                   0                 \n",
       "4                                                   0                 \n",
       "5                                                   0                 \n",
       "6                                                   0                 \n",
       "7                                                   0                 \n",
       "8                                                   0                 \n",
       "9                                                   0                 \n",
       "10                                                  0                 \n",
       "11                                                  0                 \n",
       "\n",
       "    Largest Property Use Type_Other - Lodging/Residential  \\\n",
       "0                                                   0       \n",
       "1                                                   0       \n",
       "2                                                   0       \n",
       "3                                                   0       \n",
       "4                                                   0       \n",
       "5                                                   0       \n",
       "6                                                   0       \n",
       "7                                                   0       \n",
       "8                                                   0       \n",
       "9                                                   0       \n",
       "10                                                  0       \n",
       "11                                                  0       \n",
       "\n",
       "    Largest Property Use Type_Other - Mall  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "5                                        0   \n",
       "6                                        0   \n",
       "7                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "10                                       0   \n",
       "11                                       0   \n",
       "\n",
       "    Largest Property Use Type_Other - Public Services  \\\n",
       "0                                                   0   \n",
       "1                                                   0   \n",
       "2                                                   0   \n",
       "3                                                   0   \n",
       "4                                                   0   \n",
       "5                                                   0   \n",
       "6                                                   0   \n",
       "7                                                   0   \n",
       "8                                                   0   \n",
       "9                                                   0   \n",
       "10                                                  0   \n",
       "11                                                  0   \n",
       "\n",
       "    Largest Property Use Type_Other - Recreation  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "5                                              0   \n",
       "6                                              0   \n",
       "7                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "10                                             0   \n",
       "11                                             0   \n",
       "\n",
       "    Largest Property Use Type_Other - Services  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "5                                            0   \n",
       "6                                            0   \n",
       "7                                            0   \n",
       "8                                            0   \n",
       "9                                            0   \n",
       "10                                           0   \n",
       "11                                           0   \n",
       "\n",
       "    Largest Property Use Type_Other - Specialty Hospital  \\\n",
       "0                                                   0      \n",
       "1                                                   0      \n",
       "2                                                   0      \n",
       "3                                                   0      \n",
       "4                                                   0      \n",
       "5                                                   0      \n",
       "6                                                   0      \n",
       "7                                                   0      \n",
       "8                                                   0      \n",
       "9                                                   0      \n",
       "10                                                  0      \n",
       "11                                                  0      \n",
       "\n",
       "    Largest Property Use Type_Outpatient Rehabilitation/Physical Therapy  \\\n",
       "0                                                   0                      \n",
       "1                                                   0                      \n",
       "2                                                   0                      \n",
       "3                                                   0                      \n",
       "4                                                   0                      \n",
       "5                                                   0                      \n",
       "6                                                   0                      \n",
       "7                                                   0                      \n",
       "8                                                   0                      \n",
       "9                                                   0                      \n",
       "10                                                  0                      \n",
       "11                                                  0                      \n",
       "\n",
       "    Largest Property Use Type_Parking  \\\n",
       "0                                   0   \n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "5                                   0   \n",
       "6                                   0   \n",
       "7                                   0   \n",
       "8                                   0   \n",
       "9                                   0   \n",
       "10                                  0   \n",
       "11                                  0   \n",
       "\n",
       "    Largest Property Use Type_Performing Arts  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "2                                           0   \n",
       "3                                           0   \n",
       "4                                           0   \n",
       "5                                           0   \n",
       "6                                           0   \n",
       "7                                           0   \n",
       "8                                           0   \n",
       "9                                           0   \n",
       "10                                          0   \n",
       "11                                          0   \n",
       "\n",
       "    Largest Property Use Type_Pre-school/Daycare  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "5                                              0   \n",
       "6                                              0   \n",
       "7                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "10                                             0   \n",
       "11                                             0   \n",
       "\n",
       "    Largest Property Use Type_Refrigerated Warehouse  \\\n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "4                                                  0   \n",
       "5                                                  0   \n",
       "6                                                  0   \n",
       "7                                                  0   \n",
       "8                                                  0   \n",
       "9                                                  0   \n",
       "10                                                 0   \n",
       "11                                                 0   \n",
       "\n",
       "    Largest Property Use Type_Repair Services (Vehicle, Shoe, Locksmith, etc.)  \\\n",
       "0                                                   0                            \n",
       "1                                                   0                            \n",
       "2                                                   0                            \n",
       "3                                                   0                            \n",
       "4                                                   0                            \n",
       "5                                                   0                            \n",
       "6                                                   0                            \n",
       "7                                                   0                            \n",
       "8                                                   0                            \n",
       "9                                                   0                            \n",
       "10                                                  0                            \n",
       "11                                                  0                            \n",
       "\n",
       "    Largest Property Use Type_Residence Hall/Dormitory  \\\n",
       "0                                                   0    \n",
       "1                                                   0    \n",
       "2                                                   0    \n",
       "3                                                   0    \n",
       "4                                                   0    \n",
       "5                                                   0    \n",
       "6                                                   0    \n",
       "7                                                   0    \n",
       "8                                                   0    \n",
       "9                                                   0    \n",
       "10                                                  0    \n",
       "11                                                  0    \n",
       "\n",
       "    Largest Property Use Type_Residential Care Facility  \\\n",
       "0                                                   0     \n",
       "1                                                   0     \n",
       "2                                                   0     \n",
       "3                                                   0     \n",
       "4                                                   0     \n",
       "5                                                   0     \n",
       "6                                                   0     \n",
       "7                                                   0     \n",
       "8                                                   0     \n",
       "9                                                   0     \n",
       "10                                                  0     \n",
       "11                                                  0     \n",
       "\n",
       "    Largest Property Use Type_Restaurant  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "5                                      0   \n",
       "6                                      0   \n",
       "7                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "10                                     0   \n",
       "11                                     0   \n",
       "\n",
       "    Largest Property Use Type_Retail Store  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "5                                        0   \n",
       "6                                        0   \n",
       "7                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "10                                       0   \n",
       "11                                       0   \n",
       "\n",
       "    Largest Property Use Type_Self-Storage Facility  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "5                                                 0   \n",
       "6                                                 0   \n",
       "7                                                 0   \n",
       "8                                                 0   \n",
       "9                                                 0   \n",
       "10                                                0   \n",
       "11                                                0   \n",
       "\n",
       "    Largest Property Use Type_Senior Care Community  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 1   \n",
       "4                                                 0   \n",
       "5                                                 0   \n",
       "6                                                 0   \n",
       "7                                                 0   \n",
       "8                                                 0   \n",
       "9                                                 0   \n",
       "10                                                0   \n",
       "11                                                0   \n",
       "\n",
       "    Largest Property Use Type_Social/Meeting Hall  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "5                                               0   \n",
       "6                                               0   \n",
       "7                                               0   \n",
       "8                                               0   \n",
       "9                                               0   \n",
       "10                                              0   \n",
       "11                                              0   \n",
       "\n",
       "    Largest Property Use Type_Strip Mall  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "5                                      0   \n",
       "6                                      0   \n",
       "7                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "10                                     0   \n",
       "11                                     0   \n",
       "\n",
       "    Largest Property Use Type_Supermarket/Grocery Store  \\\n",
       "0                                                   0     \n",
       "1                                                   0     \n",
       "2                                                   0     \n",
       "3                                                   0     \n",
       "4                                                   0     \n",
       "5                                                   0     \n",
       "6                                                   0     \n",
       "7                                                   0     \n",
       "8                                                   0     \n",
       "9                                                   0     \n",
       "10                                                  0     \n",
       "11                                                  0     \n",
       "\n",
       "    Largest Property Use Type_Urgent Care/Clinic/Other Outpatient  \\\n",
       "0                                                   0               \n",
       "1                                                   0               \n",
       "2                                                   0               \n",
       "3                                                   0               \n",
       "4                                                   0               \n",
       "5                                                   0               \n",
       "6                                                   0               \n",
       "7                                                   0               \n",
       "8                                                   0               \n",
       "9                                                   0               \n",
       "10                                                  0               \n",
       "11                                                  0               \n",
       "\n",
       "    Largest Property Use Type_Wholesale Club/Supercenter  \\\n",
       "0                                                   0      \n",
       "1                                                   0      \n",
       "2                                                   0      \n",
       "3                                                   0      \n",
       "4                                                   0      \n",
       "5                                                   0      \n",
       "6                                                   0      \n",
       "7                                                   0      \n",
       "8                                                   0      \n",
       "9                                                   0      \n",
       "10                                                  0      \n",
       "11                                                  0      \n",
       "\n",
       "    Largest Property Use Type_Worship Facility  \n",
       "0                                            0  \n",
       "1                                            0  \n",
       "2                                            0  \n",
       "3                                            0  \n",
       "4                                            0  \n",
       "5                                            0  \n",
       "6                                            0  \n",
       "7                                            0  \n",
       "8                                            0  \n",
       "9                                            0  \n",
       "10                                           0  \n",
       "11                                           0  \n",
       "\n",
       "[12 rows x 64 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `score` column contains the Energy Star Score, the target for our machine learning problem. The Energy Star Score is supposed to be a comparitive measurement of the energy efficiency of a building, although we saw there may be issues with how this is calculated in part one! \n",
    "\n",
    "Here's the distribution of the Energy Star Score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(8, 8)\n",
    "\n",
    "# Histogram of the Energy Star Score\n",
    "plt.hist(train_labels['score'].dropna(), bins = 100);\n",
    "plt.xlabel('Score'); plt.ylabel('Number of Buildings'); \n",
    "plt.title('ENERGY Star Score Distribution');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating and Comparing Machine Learning Models\n",
    "\n",
    "In this section we will build, train, and evalute several machine learning methods for our supervised regression task. The objective is to determine which model holds the most promise for further development (such as hyperparameter tuning). \n",
    "\n",
    "We are comparing models using the __mean absolute error__. A baseline model that guessed the median value of the score was off by an __average of 25 points__. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imputing Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard machine learning models cannot deal with missing values, and which means we have to find a way to fill these in or disard any features with missing values. Since we already removed features with more than 50% missing values in the first part, here we will focus on filling in these missing values, a process known as [imputation](https://en.wikipedia.org/wiki/Imputation_(statistics)). There are a number of methods for imputation but here we will use the relatively simple method of replacing missing values with the median of the column. ([Here is a more thorough discussion on imputing missing values](http://www.stat.columbia.edu/~gelman/arm/missing.pdf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an imputer object with a median filling strategy\n",
    "imputer = Imputer(strategy='median')\n",
    "\n",
    "# Train on the training features\n",
    "imputer.fit(train_features)\n",
    "\n",
    "# Transform both training data and testing data\n",
    "X = imputer.transform(train_features)\n",
    "X_test = imputer.transform(test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in training features:  0\n",
      "Missing values in testing features:   0\n"
     ]
    }
   ],
   "source": [
    "print('Missing values in training features: ', np.sum(np.isnan(X)))\n",
    "print('Missing values in testing features:  ', np.sum(np.isnan(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([], dtype=int64), array([], dtype=int64))\n",
      "(array([], dtype=int64), array([], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "# Make sure all values are finite\n",
    "print(np.where(~np.isfinite(X)))\n",
    "print(np.where(~np.isfinite(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After imputation, all of the features are real-valued. For more sophisticated methods of imputation (although median values usually works well) check out [this article](https://www.tandfonline.com/doi/full/10.1080/1743727X.2014.979146)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling Features\n",
    "\n",
    "The final step to take before we can build our models is to [scale the features](https://en.wikipedia.org/wiki/Feature_scaling). This is necessary because features are in different units, and we want to normalize the features so the units do not affect the algorithm. [Linear Regression and Random Forest do not require feature scaling](https://stats.stackexchange.com/questions/121886/when-should-i-apply-feature-scaling-for-my-data), but other methods, such as support vector machines and k nearest neighbors, do require it because they take into account the Euclidean distance between observations. For this reason, it is a best practice to scale features when we are comparing multiple algorithms. \n",
    "\n",
    "There are two ways to [scale features](http://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html):\n",
    "\n",
    "1. For each value, subtract the mean of the feature and divide by the standard deviation of the feature. This is known as standardization and results in each feature having a mean of 0 and a standard deviation of 1.\n",
    "2. For each value, subtract the minimum value of the feature and divide by the maximum minus the minimum for the feature (the range). This assures that all the values for a feature are between 0 and 1 and is called scaling to a range or normalization.\n",
    "\n",
    "Here is a good article about [normalization and standardization](https://machinelearningmastery.com/normalize-standardize-machine-learning-data-weka/).\n",
    "\n",
    "As with imputation, when we train the scaling object, we want to use only the training set. When we transform features, we will transform both the training set and the testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the scaler object with a range of 0-1\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "# Fit on the training data\n",
    "scaler.fit(X)\n",
    "\n",
    "# Transform both the training and testing data\n",
    "X = scaler.transform(X)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert y to one-dimensional array (vector)\n",
    "y = np.array(train_labels).reshape((-1, ))\n",
    "y_test = np.array(test_labels).reshape((-1, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models to Evaluate\n",
    "\n",
    "We will compare five different machine learning models using the great [Scikit-Learn library](http://scikit-learn.org/stable/):\n",
    "\n",
    "1. Linear Regression\n",
    "2. Support Vector Machine Regression\n",
    "3. Random Forest Regression\n",
    "4. Gradient Boosting Regression\n",
    "5. K-Nearest Neighbors Regression\n",
    "\n",
    "To compare the models, we are going to be mostly using the Scikit-Learn defaults for the model hyperparameters. Generally these will perform decently, but should be optimized before actually using a model. At first, we just want to determine the baseline performance of each model, and then we can select the best performing model for further optimization using hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate mean absolute error\n",
    "def mae(y_true, y_pred):\n",
    "    return np.mean(abs(y_true - y_pred))\n",
    "\n",
    "# Takes in a model, trains the model, and evaluates the model on the test set\n",
    "def fit_and_evaluate(model):\n",
    "    \n",
    "    # Train the model\n",
    "    model.fit(X, y)\n",
    "    \n",
    "    # Make predictions and evalute\n",
    "    model_pred = model.predict(X_test)\n",
    "    model_mae = mae(y_test, model_pred)\n",
    "    \n",
    "    # Return the performance metric\n",
    "    return model_mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Performance on the test set: MAE = 13.4651\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adhiman/anaconda3/envs/python3/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  warnings.warn(mesg, RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr_mae = fit_and_evaluate(lr)\n",
    "\n",
    "print('Linear Regression Performance on the test set: MAE = %0.4f' % lr_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine Regression Performance on the test set: MAE = 10.9337\n"
     ]
    }
   ],
   "source": [
    "svm = SVR(C = 1000, gamma = 0.1)\n",
    "svm_mae = fit_and_evaluate(svm)\n",
    "\n",
    "print('Support Vector Machine Regression Performance on the test set: MAE = %0.4f' % svm_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regression Performance on the test set: MAE = 10.0147\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestRegressor(random_state=60)\n",
    "random_forest_mae = fit_and_evaluate(random_forest)\n",
    "\n",
    "print('Random Forest Regression Performance on the test set: MAE = %0.4f' % random_forest_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosted Regression Performance on the test set: MAE = 10.0132\n"
     ]
    }
   ],
   "source": [
    "gradient_boosted = GradientBoostingRegressor(random_state=60)\n",
    "gradient_boosted_mae = fit_and_evaluate(gradient_boosted)\n",
    "\n",
    "print('Gradient Boosted Regression Performance on the test set: MAE = %0.4f' % gradient_boosted_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors Regression Performance on the test set: MAE = 13.0131\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsRegressor(n_neighbors=10)\n",
    "knn_mae = fit_and_evaluate(knn)\n",
    "\n",
    "print('K-Nearest Neighbors Regression Performance on the test set: MAE = %0.4f' % knn_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(8, 6)\n",
    "\n",
    "# Dataframe to hold the results\n",
    "model_comparison = pd.DataFrame({'model': ['Linear Regression', 'Support Vector Machine',\n",
    "                                           'Random Forest', 'Gradient Boosted',\n",
    "                                            'K-Nearest Neighbors'],\n",
    "                                 'mae': [lr_mae, svm_mae, random_forest_mae, \n",
    "                                         gradient_boosted_mae, knn_mae]})\n",
    "\n",
    "# Horizontal bar chart of test mae\n",
    "model_comparison.sort_values('mae', ascending = False).plot(x = 'model', y = 'mae', kind = 'barh',\n",
    "                                                           color = 'red', edgecolor = 'black')\n",
    "\n",
    "# Plot formatting\n",
    "plt.ylabel(''); plt.yticks(size = 14); plt.xlabel('Mean Absolute Error'); plt.xticks(size = 14)\n",
    "plt.title('Model Comparison on Test MAE', size = 20)\n",
    "plt.legend('')\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here, I am going to concentrate on optimizing the best model using hyperparamter tuning. Given the results here, I will concentrate on using the `GradientBoostingRegressor`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Optimization \n",
    "\n",
    "In machine learning, optimizing a model means finding the best set of hyperparameters for a particular problem. \n",
    "\n",
    "## Hyperparameters\n",
    "\n",
    "[Tuning the model hyperparameters](http://scikit-learn.org/stable/modules/grid_search.html) controls the balance of under vs over fitting in a model. We can try to correct for under-fitting by making a more complex model, such as using more trees in a random forest or more layers in a deep neural network. A model that underfits has high bias, and occurs when our model does not have enough capacity (degrees of freedom) to learn the relationship between the features and the target. We can try to correct for overfitting by limiting the complexity of the model and applying regularization. This might mean decreasing the degree of a polynomial regression, or adding dropout layers to a deep neural network. A model that overfits has high variance and in effect has memorized the training set. Both underfitting and overfitting lead to poor generalization performance on the test set. \n",
    "\n",
    "The problem with choosing the hyperparameters is that no set will work best across all problems. Therefore, for every new dataset, we have to find the best settings. This can be a time-consuming process, but luckily there are several options for performing this procedure in Scikit-Learn. Even better, new libraries, such as [TPOT](https://epistasislab.github.io/tpot/) by epistasis labs, is aiming to do this process automatically for you! For now, we will stick to doing this manually (sort of) in Scikit-Learn, but stay tuned for an article on automatic model selection! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning with Random Search and Cross Validation\n",
    "\n",
    "We can choose the best hyperparameters for a model through random search and cross validation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss function to be optimized\n",
    "loss = ['ls', 'lad', 'huber']\n",
    "\n",
    "# Number of trees used in the boosting process\n",
    "n_estimators = [100, 500, 900, 1100, 1500]\n",
    "\n",
    "# Maximum depth of each tree\n",
    "max_depth = [2, 3, 5, 10, 15]\n",
    "\n",
    "# Minimum number of samples per leaf\n",
    "min_samples_leaf = [1, 2, 4, 6, 8]\n",
    "\n",
    "# Minimum number of samples to split a node\n",
    "min_samples_split = [2, 4, 6, 10]\n",
    "\n",
    "# Maximum number of features to consider for making splits\n",
    "max_features = ['auto', 'sqrt', 'log2', None]\n",
    "\n",
    "# Define the grid of hyperparameters to search\n",
    "hyperparameter_grid = {'loss': loss,\n",
    "                       'n_estimators': n_estimators,\n",
    "                       'max_depth': max_depth,\n",
    "                       'min_samples_leaf': min_samples_leaf,\n",
    "                       'min_samples_split': min_samples_split,\n",
    "                       'max_features': max_features}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code below, we create the Randomized Search Object passing in the following parameters:\n",
    "\n",
    "* `estimator`: the model\n",
    "* `param_distributions`: the distribution of parameters we defined\n",
    "* `cv` the number of folds to use for k-fold cross validation\n",
    "* `n_iter`: the number of different combinations to try\n",
    "* `scoring`: which metric to use when evaluating candidates\n",
    "* `n_jobs`: number of cores to run in parallel (-1 will use all available)\n",
    "* `verbose`: how much information to display (1 displays a limited amount) \n",
    "* `return_train_score`: return the training score for each cross-validation fold\n",
    "* `random_state`: fixes the random number generator used so we get the same results every run\n",
    "\n",
    "The Randomized Search Object is trained the same way as any other scikit-learn model. After training, we can compare all the different hyperparameter combinations and find the best performing one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the model to use for hyperparameter tuning\n",
    "model = GradientBoostingRegressor(random_state = 42)\n",
    "\n",
    "# Set up the random search with 4-fold cross validation\n",
    "random_cv = RandomizedSearchCV(estimator=model,\n",
    "                               param_distributions=hyperparameter_grid,\n",
    "                               cv=4, n_iter=25, \n",
    "                               scoring = 'neg_mean_absolute_error',\n",
    "                               n_jobs = -1, verbose = 1, \n",
    "                               return_train_score = True,\n",
    "                               random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 25 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  8.5min\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 14.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10.7 s, sys: 301 ms, total: 11 s\n",
      "Wall time: 15min 3s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=4, error_score='raise',\n",
       "          estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=100, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=25, n_jobs=-1,\n",
       "          param_distributions={'loss': ['ls', 'lad', 'huber'], 'n_estimators': [100, 500, 900, 1100, 1500], 'max_depth': [2, 3, 5, 10, 15], 'min_samples_leaf': [1, 2, 4, 6, 8], 'min_samples_split': [2, 4, 6, 10], 'max_features': ['auto', 'sqrt', 'log2', None]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score=True, scoring='neg_mean_absolute_error',\n",
       "          verbose=1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Fit on the training data\n",
    "random_cv.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn uses the negative mean absolute error for evaluation because it wants a metric to maximize. Therefore, a better score will be closer to 0. We can get the results of the randomized search into a dataframe, and sort the values by performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_loss</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21.856699</td>\n",
       "      <td>0.453608</td>\n",
       "      <td>0.029032</td>\n",
       "      <td>0.002364</td>\n",
       "      <td>500</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>lad</td>\n",
       "      <td>{'n_estimators': 500, 'min_samples_split': 6, ...</td>\n",
       "      <td>-8.859381</td>\n",
       "      <td>-8.773966</td>\n",
       "      <td>-9.325139</td>\n",
       "      <td>-9.036779</td>\n",
       "      <td>-8.998761</td>\n",
       "      <td>0.210901</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.027368</td>\n",
       "      <td>-6.837958</td>\n",
       "      <td>-6.869084</td>\n",
       "      <td>-6.840749</td>\n",
       "      <td>-6.893790</td>\n",
       "      <td>0.078077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.586591</td>\n",
       "      <td>0.771180</td>\n",
       "      <td>0.035564</td>\n",
       "      <td>0.006835</td>\n",
       "      <td>500</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>huber</td>\n",
       "      <td>{'n_estimators': 500, 'min_samples_split': 6, ...</td>\n",
       "      <td>-8.868788</td>\n",
       "      <td>-8.904791</td>\n",
       "      <td>-9.315304</td>\n",
       "      <td>-9.075321</td>\n",
       "      <td>-9.041004</td>\n",
       "      <td>0.176509</td>\n",
       "      <td>2</td>\n",
       "      <td>-4.482073</td>\n",
       "      <td>-4.326121</td>\n",
       "      <td>-4.823009</td>\n",
       "      <td>-4.229809</td>\n",
       "      <td>-4.465253</td>\n",
       "      <td>0.225313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.309776</td>\n",
       "      <td>0.190722</td>\n",
       "      <td>0.022591</td>\n",
       "      <td>0.004532</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>auto</td>\n",
       "      <td>3</td>\n",
       "      <td>huber</td>\n",
       "      <td>{'n_estimators': 500, 'min_samples_split': 4, ...</td>\n",
       "      <td>-9.134318</td>\n",
       "      <td>-9.042461</td>\n",
       "      <td>-9.441179</td>\n",
       "      <td>-9.152240</td>\n",
       "      <td>-9.192518</td>\n",
       "      <td>0.149456</td>\n",
       "      <td>3</td>\n",
       "      <td>-7.007604</td>\n",
       "      <td>-7.088986</td>\n",
       "      <td>-6.913108</td>\n",
       "      <td>-6.940327</td>\n",
       "      <td>-6.987506</td>\n",
       "      <td>0.067939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.719713</td>\n",
       "      <td>0.051915</td>\n",
       "      <td>0.009181</td>\n",
       "      <td>0.001679</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>auto</td>\n",
       "      <td>5</td>\n",
       "      <td>ls</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 2, ...</td>\n",
       "      <td>-9.101610</td>\n",
       "      <td>-9.026912</td>\n",
       "      <td>-9.456259</td>\n",
       "      <td>-9.201033</td>\n",
       "      <td>-9.196413</td>\n",
       "      <td>0.162211</td>\n",
       "      <td>4</td>\n",
       "      <td>-7.302003</td>\n",
       "      <td>-7.312645</td>\n",
       "      <td>-7.173397</td>\n",
       "      <td>-7.386171</td>\n",
       "      <td>-7.293554</td>\n",
       "      <td>0.076569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.424583</td>\n",
       "      <td>0.237672</td>\n",
       "      <td>0.019565</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>auto</td>\n",
       "      <td>3</td>\n",
       "      <td>ls</td>\n",
       "      <td>{'n_estimators': 500, 'min_samples_split': 4, ...</td>\n",
       "      <td>-9.146159</td>\n",
       "      <td>-9.199358</td>\n",
       "      <td>-9.699696</td>\n",
       "      <td>-9.357220</td>\n",
       "      <td>-9.350555</td>\n",
       "      <td>0.215966</td>\n",
       "      <td>5</td>\n",
       "      <td>-7.089709</td>\n",
       "      <td>-7.103749</td>\n",
       "      <td>-7.021418</td>\n",
       "      <td>-7.080088</td>\n",
       "      <td>-7.073741</td>\n",
       "      <td>0.031358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101.891827</td>\n",
       "      <td>2.877972</td>\n",
       "      <td>0.118077</td>\n",
       "      <td>0.010391</td>\n",
       "      <td>1100</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>huber</td>\n",
       "      <td>{'n_estimators': 1100, 'min_samples_split': 10...</td>\n",
       "      <td>-9.261256</td>\n",
       "      <td>-9.243867</td>\n",
       "      <td>-9.528538</td>\n",
       "      <td>-9.473096</td>\n",
       "      <td>-9.376652</td>\n",
       "      <td>0.125816</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.378166</td>\n",
       "      <td>-0.459465</td>\n",
       "      <td>-0.393491</td>\n",
       "      <td>-0.370483</td>\n",
       "      <td>-0.400401</td>\n",
       "      <td>0.035092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30.444557</td>\n",
       "      <td>0.532153</td>\n",
       "      <td>0.049195</td>\n",
       "      <td>0.005448</td>\n",
       "      <td>1100</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>auto</td>\n",
       "      <td>3</td>\n",
       "      <td>lad</td>\n",
       "      <td>{'n_estimators': 1100, 'min_samples_split': 6,...</td>\n",
       "      <td>-9.379335</td>\n",
       "      <td>-9.382576</td>\n",
       "      <td>-9.473980</td>\n",
       "      <td>-9.290880</td>\n",
       "      <td>-9.381693</td>\n",
       "      <td>0.064740</td>\n",
       "      <td>7</td>\n",
       "      <td>-8.311796</td>\n",
       "      <td>-8.658903</td>\n",
       "      <td>-7.820534</td>\n",
       "      <td>-8.017995</td>\n",
       "      <td>-8.202307</td>\n",
       "      <td>0.316303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.335977</td>\n",
       "      <td>2.567601</td>\n",
       "      <td>0.083129</td>\n",
       "      <td>0.018528</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>auto</td>\n",
       "      <td>10</td>\n",
       "      <td>huber</td>\n",
       "      <td>{'n_estimators': 500, 'min_samples_split': 4, ...</td>\n",
       "      <td>-9.385744</td>\n",
       "      <td>-9.288317</td>\n",
       "      <td>-9.572674</td>\n",
       "      <td>-9.429825</td>\n",
       "      <td>-9.419115</td>\n",
       "      <td>0.102366</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.164859</td>\n",
       "      <td>-0.193613</td>\n",
       "      <td>-0.164415</td>\n",
       "      <td>-0.141062</td>\n",
       "      <td>-0.165987</td>\n",
       "      <td>0.018629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>39.470272</td>\n",
       "      <td>0.934748</td>\n",
       "      <td>0.067222</td>\n",
       "      <td>0.011553</td>\n",
       "      <td>1500</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>ls</td>\n",
       "      <td>{'n_estimators': 1500, 'min_samples_split': 4,...</td>\n",
       "      <td>-9.246573</td>\n",
       "      <td>-9.317156</td>\n",
       "      <td>-9.798950</td>\n",
       "      <td>-9.424668</td>\n",
       "      <td>-9.446787</td>\n",
       "      <td>0.212942</td>\n",
       "      <td>9</td>\n",
       "      <td>-4.821677</td>\n",
       "      <td>-4.876202</td>\n",
       "      <td>-4.776650</td>\n",
       "      <td>-4.729506</td>\n",
       "      <td>-4.801009</td>\n",
       "      <td>0.054284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.326262</td>\n",
       "      <td>0.404379</td>\n",
       "      <td>0.029943</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>500</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>log2</td>\n",
       "      <td>5</td>\n",
       "      <td>huber</td>\n",
       "      <td>{'n_estimators': 500, 'min_samples_split': 6, ...</td>\n",
       "      <td>-9.393840</td>\n",
       "      <td>-9.289175</td>\n",
       "      <td>-9.750806</td>\n",
       "      <td>-9.407503</td>\n",
       "      <td>-9.460295</td>\n",
       "      <td>0.173829</td>\n",
       "      <td>10</td>\n",
       "      <td>-5.684121</td>\n",
       "      <td>-5.742441</td>\n",
       "      <td>-5.571781</td>\n",
       "      <td>-5.666476</td>\n",
       "      <td>-5.666205</td>\n",
       "      <td>0.061337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "12      21.856699      0.453608         0.029032        0.002364   \n",
       "3       23.586591      0.771180         0.035564        0.006835   \n",
       "9       12.309776      0.190722         0.022591        0.004532   \n",
       "0        3.719713      0.051915         0.009181        0.001679   \n",
       "7       10.424583      0.237672         0.019565        0.000689   \n",
       "10     101.891827      2.877972         0.118077        0.010391   \n",
       "19      30.444557      0.532153         0.049195        0.005448   \n",
       "2       70.335977      2.567601         0.083129        0.018528   \n",
       "16      39.470272      0.934748         0.067222        0.011553   \n",
       "21       7.326262      0.404379         0.029943        0.000968   \n",
       "\n",
       "   param_n_estimators param_min_samples_split param_min_samples_leaf  \\\n",
       "12                500                       6                      6   \n",
       "3                 500                       6                      8   \n",
       "9                 500                       4                      2   \n",
       "0                 100                       2                      6   \n",
       "7                 500                       4                      6   \n",
       "10               1100                      10                      6   \n",
       "19               1100                       6                      2   \n",
       "2                 500                       4                      2   \n",
       "16               1500                       4                      6   \n",
       "21                500                       6                      4   \n",
       "\n",
       "   param_max_features param_max_depth param_loss  \\\n",
       "12               None               5        lad   \n",
       "3                None               5      huber   \n",
       "9                auto               3      huber   \n",
       "0                auto               5         ls   \n",
       "7                auto               3         ls   \n",
       "10               None              10      huber   \n",
       "19               auto               3        lad   \n",
       "2                auto              10      huber   \n",
       "16               None               3         ls   \n",
       "21               log2               5      huber   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "12  {'n_estimators': 500, 'min_samples_split': 6, ...          -8.859381   \n",
       "3   {'n_estimators': 500, 'min_samples_split': 6, ...          -8.868788   \n",
       "9   {'n_estimators': 500, 'min_samples_split': 4, ...          -9.134318   \n",
       "0   {'n_estimators': 100, 'min_samples_split': 2, ...          -9.101610   \n",
       "7   {'n_estimators': 500, 'min_samples_split': 4, ...          -9.146159   \n",
       "10  {'n_estimators': 1100, 'min_samples_split': 10...          -9.261256   \n",
       "19  {'n_estimators': 1100, 'min_samples_split': 6,...          -9.379335   \n",
       "2   {'n_estimators': 500, 'min_samples_split': 4, ...          -9.385744   \n",
       "16  {'n_estimators': 1500, 'min_samples_split': 4,...          -9.246573   \n",
       "21  {'n_estimators': 500, 'min_samples_split': 6, ...          -9.393840   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  mean_test_score  \\\n",
       "12          -8.773966          -9.325139          -9.036779        -8.998761   \n",
       "3           -8.904791          -9.315304          -9.075321        -9.041004   \n",
       "9           -9.042461          -9.441179          -9.152240        -9.192518   \n",
       "0           -9.026912          -9.456259          -9.201033        -9.196413   \n",
       "7           -9.199358          -9.699696          -9.357220        -9.350555   \n",
       "10          -9.243867          -9.528538          -9.473096        -9.376652   \n",
       "19          -9.382576          -9.473980          -9.290880        -9.381693   \n",
       "2           -9.288317          -9.572674          -9.429825        -9.419115   \n",
       "16          -9.317156          -9.798950          -9.424668        -9.446787   \n",
       "21          -9.289175          -9.750806          -9.407503        -9.460295   \n",
       "\n",
       "    std_test_score  rank_test_score  split0_train_score  split1_train_score  \\\n",
       "12        0.210901                1           -7.027368           -6.837958   \n",
       "3         0.176509                2           -4.482073           -4.326121   \n",
       "9         0.149456                3           -7.007604           -7.088986   \n",
       "0         0.162211                4           -7.302003           -7.312645   \n",
       "7         0.215966                5           -7.089709           -7.103749   \n",
       "10        0.125816                6           -0.378166           -0.459465   \n",
       "19        0.064740                7           -8.311796           -8.658903   \n",
       "2         0.102366                8           -0.164859           -0.193613   \n",
       "16        0.212942                9           -4.821677           -4.876202   \n",
       "21        0.173829               10           -5.684121           -5.742441   \n",
       "\n",
       "    split2_train_score  split3_train_score  mean_train_score  std_train_score  \n",
       "12           -6.869084           -6.840749         -6.893790         0.078077  \n",
       "3            -4.823009           -4.229809         -4.465253         0.225313  \n",
       "9            -6.913108           -6.940327         -6.987506         0.067939  \n",
       "0            -7.173397           -7.386171         -7.293554         0.076569  \n",
       "7            -7.021418           -7.080088         -7.073741         0.031358  \n",
       "10           -0.393491           -0.370483         -0.400401         0.035092  \n",
       "19           -7.820534           -8.017995         -8.202307         0.316303  \n",
       "2            -0.164415           -0.141062         -0.165987         0.018629  \n",
       "16           -4.776650           -4.729506         -4.801009         0.054284  \n",
       "21           -5.571781           -5.666476         -5.666205         0.061337  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all of the cv results and sort by the test performance\n",
    "random_results = pd.DataFrame(random_cv.cv_results_).sort_values('mean_test_score', ascending = False)\n",
    "\n",
    "random_results.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='lad', max_depth=5, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=6,\n",
       "             min_samples_split=6, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=500, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than evaluating all of these settings again, I will focus on a single one, the number of trees in the forest (`n_estimators`). By varying only one hyperparameter, we can directly observe how it affects performance. In the case of the number of trees, we would expect to see a significant affect on the amount of under vs overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a range of trees to evaluate\n",
    "trees_grid = {'n_estimators': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800]}\n",
    "\n",
    "model = GradientBoostingRegressor(loss = 'lad', max_depth = 5,\n",
    "                                  min_samples_leaf = 6,\n",
    "                                  min_samples_split = 6,\n",
    "                                  max_features = None,\n",
    "                                  random_state = 42)\n",
    "\n",
    "# Grid Search Object using the trees range and the random forest model\n",
    "grid_search = GridSearchCV(estimator = model, param_grid=trees_grid, cv = 4, \n",
    "                           scoring = 'neg_mean_absolute_error', verbose = 1,\n",
    "                           n_jobs = -1, return_train_score = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 15 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  3.2min\n",
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:  5.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.5 s, sys: 261 ms, total: 16.7 s\n",
      "Wall time: 5min 46s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, error_score='raise',\n",
       "       estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='lad', max_depth=5, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=6,\n",
       "             min_samples_split=6, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=100, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'n_estimators': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_mean_absolute_error', verbose=1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Fit the grid search\n",
    "grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the results into a dataframe\n",
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "\n",
    "# Plot the training and testing error vs number of trees\n",
    "figsize(8, 8)\n",
    "plt.plot(results['param_n_estimators'], -1 * results['mean_test_score'], label = 'Testing Error')\n",
    "plt.plot(results['param_n_estimators'], -1 * results['mean_train_score'], label = 'Training Error')\n",
    "plt.xlabel('Number of Trees'); plt.ylabel('Mean Abosolute Error'); plt.legend();\n",
    "plt.title('Performance vs Number of Trees');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>35.597751</td>\n",
       "      <td>0.752076</td>\n",
       "      <td>0.035664</td>\n",
       "      <td>0.004696</td>\n",
       "      <td>800</td>\n",
       "      <td>{'n_estimators': 800}</td>\n",
       "      <td>-8.825018</td>\n",
       "      <td>-8.757257</td>\n",
       "      <td>-9.297520</td>\n",
       "      <td>-9.046451</td>\n",
       "      <td>-8.981504</td>\n",
       "      <td>0.211453</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.739448</td>\n",
       "      <td>-6.648109</td>\n",
       "      <td>-6.486269</td>\n",
       "      <td>-6.530287</td>\n",
       "      <td>-6.601028</td>\n",
       "      <td>0.099437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>33.876809</td>\n",
       "      <td>1.039307</td>\n",
       "      <td>0.045848</td>\n",
       "      <td>0.008303</td>\n",
       "      <td>750</td>\n",
       "      <td>{'n_estimators': 750}</td>\n",
       "      <td>-8.830294</td>\n",
       "      <td>-8.750657</td>\n",
       "      <td>-9.299357</td>\n",
       "      <td>-9.048696</td>\n",
       "      <td>-8.982193</td>\n",
       "      <td>0.213125</td>\n",
       "      <td>2</td>\n",
       "      <td>-6.772908</td>\n",
       "      <td>-6.660453</td>\n",
       "      <td>-6.520818</td>\n",
       "      <td>-6.579989</td>\n",
       "      <td>-6.633542</td>\n",
       "      <td>0.094501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>33.953599</td>\n",
       "      <td>0.960626</td>\n",
       "      <td>0.046969</td>\n",
       "      <td>0.006421</td>\n",
       "      <td>700</td>\n",
       "      <td>{'n_estimators': 700}</td>\n",
       "      <td>-8.835777</td>\n",
       "      <td>-8.754819</td>\n",
       "      <td>-9.313318</td>\n",
       "      <td>-9.052721</td>\n",
       "      <td>-8.989100</td>\n",
       "      <td>0.216536</td>\n",
       "      <td>3</td>\n",
       "      <td>-6.812928</td>\n",
       "      <td>-6.695770</td>\n",
       "      <td>-6.611240</td>\n",
       "      <td>-6.624919</td>\n",
       "      <td>-6.686214</td>\n",
       "      <td>0.079884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29.550928</td>\n",
       "      <td>0.719095</td>\n",
       "      <td>0.033206</td>\n",
       "      <td>0.002663</td>\n",
       "      <td>550</td>\n",
       "      <td>{'n_estimators': 550}</td>\n",
       "      <td>-8.853451</td>\n",
       "      <td>-8.755897</td>\n",
       "      <td>-9.325442</td>\n",
       "      <td>-9.041991</td>\n",
       "      <td>-8.994138</td>\n",
       "      <td>0.217136</td>\n",
       "      <td>4</td>\n",
       "      <td>-7.003413</td>\n",
       "      <td>-6.781878</td>\n",
       "      <td>-6.809121</td>\n",
       "      <td>-6.745712</td>\n",
       "      <td>-6.835031</td>\n",
       "      <td>0.099783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30.429087</td>\n",
       "      <td>0.687990</td>\n",
       "      <td>0.039745</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>650</td>\n",
       "      <td>{'n_estimators': 650}</td>\n",
       "      <td>-8.850857</td>\n",
       "      <td>-8.754361</td>\n",
       "      <td>-9.325111</td>\n",
       "      <td>-9.051181</td>\n",
       "      <td>-8.995319</td>\n",
       "      <td>0.218405</td>\n",
       "      <td>5</td>\n",
       "      <td>-6.864375</td>\n",
       "      <td>-6.721452</td>\n",
       "      <td>-6.708191</td>\n",
       "      <td>-6.666758</td>\n",
       "      <td>-6.740194</td>\n",
       "      <td>0.074480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "14      35.597751      0.752076         0.035664        0.004696   \n",
       "13      33.876809      1.039307         0.045848        0.008303   \n",
       "12      33.953599      0.960626         0.046969        0.006421   \n",
       "9       29.550928      0.719095         0.033206        0.002663   \n",
       "11      30.429087      0.687990         0.039745        0.002721   \n",
       "\n",
       "   param_n_estimators                 params  split0_test_score  \\\n",
       "14                800  {'n_estimators': 800}          -8.825018   \n",
       "13                750  {'n_estimators': 750}          -8.830294   \n",
       "12                700  {'n_estimators': 700}          -8.835777   \n",
       "9                 550  {'n_estimators': 550}          -8.853451   \n",
       "11                650  {'n_estimators': 650}          -8.850857   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  mean_test_score  \\\n",
       "14          -8.757257          -9.297520          -9.046451        -8.981504   \n",
       "13          -8.750657          -9.299357          -9.048696        -8.982193   \n",
       "12          -8.754819          -9.313318          -9.052721        -8.989100   \n",
       "9           -8.755897          -9.325442          -9.041991        -8.994138   \n",
       "11          -8.754361          -9.325111          -9.051181        -8.995319   \n",
       "\n",
       "    std_test_score  rank_test_score  split0_train_score  split1_train_score  \\\n",
       "14        0.211453                1           -6.739448           -6.648109   \n",
       "13        0.213125                2           -6.772908           -6.660453   \n",
       "12        0.216536                3           -6.812928           -6.695770   \n",
       "9         0.217136                4           -7.003413           -6.781878   \n",
       "11        0.218405                5           -6.864375           -6.721452   \n",
       "\n",
       "    split2_train_score  split3_train_score  mean_train_score  std_train_score  \n",
       "14           -6.486269           -6.530287         -6.601028         0.099437  \n",
       "13           -6.520818           -6.579989         -6.633542         0.094501  \n",
       "12           -6.611240           -6.624919         -6.686214         0.079884  \n",
       "9            -6.809121           -6.745712         -6.835031         0.099783  \n",
       "11           -6.708191           -6.666758         -6.740194         0.074480  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.sort_values('mean_test_score', ascending = False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this plot, it's pretty clear that our model is [overfitting](https://elitedatascience.com/overfitting-in-machine-learning)! The training error is significantly lower than the testing error, which shows that the model is learning the training data very well but then is not able to generalize to the test data as well. Moveover, as the number of trees increases, the amount of overfitting increases. Both the test and training error decrease as the number of trees increase but the training error decreases more rapidly. \n",
    "\n",
    "Based on the cross validation results, the best model using 800 trees and achieves a cross validation error under 9. This indicates that the average cross-validation estimate of the Energy Star Score is within 9 points of the true answer! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Final Model on the Test Set\n",
    "\n",
    "We will use the best model from hyperparameter tuning to make predictions on the testing set. Remember, our model has never seen the test set before, so this performance should be a good indicator of how the model would perform if deployed in the real world. \n",
    "\n",
    "For comparison, we can also look at the performance of the default model. The code below creates the final model, trains it (with timing), and evaluates on the test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='lad', max_depth=5, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=6,\n",
       "             min_samples_split=6, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=800, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Default model\n",
    "default_model = GradientBoostingRegressor(random_state = 42)\n",
    "\n",
    "# Select the best model\n",
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "final_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.03 s ± 73.6 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 1 -r 5\n",
    "default_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.3 s ± 136 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 1 -r 5\n",
    "final_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default model performance on the test set: MAE = 10.0118.\n",
      "Final model performance on the test set:   MAE = 9.0446.\n"
     ]
    }
   ],
   "source": [
    "default_pred = default_model.predict(X_test)\n",
    "final_pred = final_model.predict(X_test)\n",
    "\n",
    "print('Default model performance on the test set: MAE = %0.4f.' % mae(y_test, default_pred))\n",
    "print('Final model performance on the test set:   MAE = %0.4f.' % mae(y_test, final_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final model does out-perform the baseline model by about 10%, but at the cost of significantly increased running time (it's about 12 times slower on my machine). Machine learning is often a field of tradeoffs: bias vs variance, acccuracy vs interpretability, accuracy vs running time, and the final decision of which model to use depends on the situation. Here, the increase in run time is not an impediment, because while the relative difference is large, the absolute magnitude of the training time is not significant. In a different situation, the balance might not be the same so we would need to consider what we are optimizing for and the limitations we have to work with. \n",
    "\n",
    "To get a sense of the predictions, we can plot the distribution of true values on the test set and the predicted values on the test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(8, 8)\n",
    "\n",
    "# Density plot of the final predictions and the test values\n",
    "sns.kdeplot(final_pred, label = 'Predictions')\n",
    "sns.kdeplot(y_test, label = 'Values')\n",
    "\n",
    "# Label the plot\n",
    "plt.xlabel('Energy Star Score'); plt.ylabel('Density');\n",
    "plt.title('Test Values and Predictions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution looks to be nearly the same although the density of the predicted values is closer to the median of the test values rather than to the actual peak at 100. It appears the model might be less accurate at predicting the extreme values and instead predicts values closer to the median. \n",
    "\n",
    "Another diagnostic plot is a histogram of the residuals. Ideally, we would hope that the residuals are normally distributed, meaning that the model is wrong the same amount in both directions (high and low). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DcHCw5a7Br776is2bN1c2XCuBgYG8/vrrFBUVkZaWxrfffsvWrVv56aefWLt2LefOnePtt9+ucnvS8GhlSKSajh07xrfffgtwxb0GRUVFZGRk8MsvvwCll9R69+7Niy++yGeffcbDDz8MwEcffVS7nYYyz8sxM/fN29vbsinbvGG1ouf1nDhx4k/3x7y3x/z+5TE/S6a8PSo1wdyHPXv2VHjJxdyHxo0bc/3119dKP8waN27Mq6++ip2dHcePH2fatGlW581PhM7Ozq7wZ3Py5ElSU1OtVnqOHTvGN998Q0lJCfb29tx6662MHj2aZcuWWTbO//zzz5bLcOW50s8rLy+PnJycMsev5rP07rvvUlJSwoABA5g3bx4PP/wwN998s2VV6tdff62wn5cqLi4mOzvbsiHbycmJsLAwnnnmGRISEnj11VcB+PTTT8tdfRTjUBgSqaapU6dSXFxMUFCQ1d1H5fnkk0/o378/sbGxZf5mbG9vb7kl/tJfxLX1pZyffvppuSsgy5cvB7Aai/l5M0eOHClzW7vJZCr3QY1w5QcFXuqee+4BYOvWrRw5cqTM+fz8fMu3wN9xxx1XbO9qhIaG4ubmRm5uruW7xi63dOlSALp3735NvjA1NDSUyMhIAD788EO2bdtmORcUFISvry/nzp3jww8/LPf1b7zxBsOGDbN8X1pRUREPPvggjz32WLm3v3fp0sVyybGyDeLh4eE4ODjw448/lhuIzA/cvJx5Ram8MH748GH27NlT5rj581DefrKSkhLLe11pQ/uJEye49957+dvf/lbuZcDbb7/d8ueq7D+ShkthSKSKMjMziYmJ4bPPPsPR0ZGpU6da3fJbnnvuuQdXV1f279/PtGnTrC4JHD16lIULFwJYns8Df1wK+e23367qScoVOXnyJOPGjeP3338HSn+RvP3223z88cc4Ozvz//7f/7PUduzYEQcHB86ePcvMmTMtv3TOnDnD5MmTy93QDH9cRrr0GTcV6dKlC926daOoqIjRo0db3SZ9/Phxnn76aY4fP46fnx+DBg262mFXytXVlcceewyAF1980equtcLCQqZPn87nn3+Oo6MjMTExtdKH8jz77LM0adIEKL10Zv4c2NnZ8dRTTwEwbdo0q+fjmEwm4uPjSUhIALBssHZycrLssXn++eetQklRUREzZszgwoUL+Pr6Vrg5GkqfvG6+4ywmJsbq57VlyxbLLf+X69y5M1D6kMxNmzZZjmdlZRETE1NuCDE/wHLVqlWcPHnScvy3334jNjbW8rDT8p4/danmzZsTFhZGcXEx48ePtwpEBQUFzJgxAyh9iGplN0JIw6c9QyKXeeaZZ6w2RRcWFvLbb79ZlvPd3Nz473//a/mffGVcXV35z3/+w5gxY1i8eDFr1qzhxhtvpKioiEOHDmEymbjlllusHqx30003YWdnx4kTJ+jTpw833HCDZfXmz+jVqxdbtmzh7rvvJiAggOPHj3Py5EkaNWrE9OnTrZ7z06RJE0aOHMl7773He++9x7p167j++us5ePAghYWFPPnkk+U+4Tk4OBiATZs20b9/f8LCwnjhhRcq7NMbb7xBVFQUGRkZPPDAAwQGBuLo6MjevXsxmUz4+voye/ZsywMOa8OTTz7JgQMHWL9+PdHR0bRs2ZKmTZty4MABCgoKaNy4Ma+88go333xzrfXhcp6enowfP55JkyZx4MABFi5cyJNPPgmU3sK+d+9eFi1axLPPPsv06dNp3rw5hw8ftnythvmrX8wmTpzIjh072Lt3Lw888AB+fn64urqSnZ3N77//jrOzM9OmTbvit9SbH5K5fft2HnzwQdq2bcvZs2fJysoiJCSEgwcPlvlqj549e9KxY0d2795NTEwM/v7+ODk5sX//fjw9PRk6dCjLli2zes2TTz7J119/TUZGBj179iQgIACTyURmZiYmk4mwsDB27NhBUVEReXl5VvuqLvfqq68ycOBAtm/fTq9evbjxxhtxdHQkKyuLs2fP4uXlxdSpU6v185GGRytDIpf58ccfre7O2bNnDyaTidDQUP7v//6PTz75xPJ9YVXRu3dvPvjgA+677z5cXV3Zu3cvx48f5+abb2bixIn873//s/plHxAQwCuvvMKNN97IiRMnyM7Otvrb8dUKDw/n3XffpV27duzfv58LFy7Qq1cvVqxYwQMPPFCmfuLEibzyyiu0b9+eM2fOkJ2dTVhYGMuXL6dPnz7lvsfDDz/MqFGjaNq0KZmZmaSnp1faJx8fH1auXMmECRO4+eabOXr0KIcOHSIwMJBnnnmGxMTEaj+DqbocHBx44403mDlzJrfffjsFBQXs2bPHshKSmJhoeXbPtTRgwABL4J43bx7Z2dmWc//85z9ZuHAhPXv2pLi42DLPd9xxB3PmzCmziuXl5cX//vc/oqKiCAgI4Ndff2Xv3r14eHgwePBg1q5dW6WnmLu6urJw4UKee+45goKCyMzM5Ny5c4wYMYL4+Phyw5SDgwOLFi1i9OjRtG7dmiNHjpCbm8vDDz9MUlKSZR/UpTp16kRiYiL33XcfTZs2Zd++fZw8eZKQkBBeffVV3n//fcvXlFz+/YCX8/PzY82aNTz66KO0bNmS7OxsMjMzad68OaNGjWLdunWWx0uIcdmVVOXivoiIiEgDpZUhERERMTSFIRERETE0hSERERExNIUhERERMTSFIRERETE0PWfoCk6caFhf4OftfR25uRV/t5CRaC5KaR5KaR5KaR7+oLko1ZDmwcen/IdramXIYBo1qvyJyUaiuSileSileSilefiD5qKUEeZBYUhEREQMTWFIREREDE1hSERERAxNYUhEREQMTWFIREREDE1hSERERAzNZsJQbm4uL730EnfeeSft27enZ8+e/Oc//+HcuXNWdSaTiUWLFtGvXz86duxIr169ePvtt7lw4UK57SYlJREREUGnTp246667mD59OgUFBddiSCIiIlIP2EQYKigoYOjQofzvf/8jICCAESNGcP3117Nw4UIee+wxTCaTpfbll19m+vTpeHl5MXLkSJo3b86sWbOIjY0t0+78+fOZOHEixcXFDB8+nHbt2rFo0SIef/xxioqKruUQRURExEbZxBOoV6xYwYEDBxg5ciSTJ08GoKSkhAkTJrB27VrWrl3Lww8/zM6dO1mxYgV9+vQhLi4OOzs7SkpKeO6550hKSmLLli2Eh4cDcPToUWbNmkVISAhLlizB0dERgLi4OObMmcPKlSsZPnx4nY1ZREREbINNrAz98MMPADzyyCOWY3Z2dgwaNAiAtLQ0AJYuXQrAmDFjsLOzs9Q9++yz2NnZsWrVKsvrV6xYgclkIjo62hKEAEaPHo2bm5tVrYiIiBiXTYQhLy8voHQ151LHjx8HoEmTJgCkpqbi7e1N27ZtreqaN2+Ov78/KSkplmPmP3ft2tWq1tnZmU6dOpGens6ZMw3re8dERESk+mwiDD3yyCM4Ojoyffp0duzYwblz59i2bRuvv/467u7uPPLIIxQVFfHrr79y4403ltuGr68vv//+O6dOnQIgKyuLZs2a4ebmVm4twMGDB2tvUCIiIlIv2EQYat++Pe+99x7nz59n6NChdOrUiZEjR+Lg4MDy5ctp1aoVeXl5ALi7l/+Ns+bj5tWevLy8K9bm5+fX9FBERESknrGJDdQ5OTnMmDGDEydOEB4eTkBAAD/++CPbt2/nxRdfZP78+ZY7ypycnMptw3y8sLAQKL0Fv6q1lfH2vq7BfWOvj0/5IdGINBelNA+lNA+lNA9/0FyUaujzYBNhKDY2lp07dzJz5kz69etnOb5o0SKmT5/OCy+8wEsvvQRQ4fOEzLfKN27cGAAXF5cq11YmN/ds1QdSD/j4uHPihPZKgebCTPNQSvNQSvPwB81FqYY0DxWFujq/TPbrr7/y7bff0rVrV6sgBDBq1CiCgoLYvHkzjo6O2NvbV3hpy3x5zHwJzMPDo8IN0pfXioiIiHHVeRg6duwYAG3atCn3fGBgIMXFxfz222+0bNmSw4cPl1t3+PBhvL29LXem+fv7k5OTw/nz58vUHjlyBHt7e1q3bl1DoxAREZH6qs7DULNmzQDIzMws9/yhQ4ews7OjadOmhIaGcuLEiTJ3gR0/fpxDhw7RqVMny7HQ0FCKi4tJTU21qi0sLCQtLY2goKBy7zQTERERY6nzPUN+fn7ccsstbN++nU8//ZTevXtbzq1atYr09HTuvPNOvLy8iIiI4MMPP2TmzJm8+eab2NvbU1JSwowZMygpKSEyMtLy2v79+zN//nxmz55NWFiYZdP0vHnzyM/Pt6oVEeO6ePEimZkHyM1149Spmr/D1N+/DQ4ODesmDJGGps7DEMC0adMYMWIEY8eOtdxNtmfPHr788kt8fHwsm6dvv/12+vXrx4YNG4iMjKRbt258//33pKam0qdPH+655x5Lm23atCEqKooFCxYQERFBeHg4+/btIzk5mc6dOzN48OA6Gq2I2JLMzAOcvi2UJkCTGm77IJD57Q4CA2+q4ZZFpCbZlZSUlNR1J6D0IYlvv/02X3/9Nbm5uTRt2pR77rmHMWPGcP3111vqLly4wDvvvENiYiLHjx+nZcuW/PWvf+Xvf/97mVvpS0pKWLZsGcuWLSMrKwsfHx/uvfdexowZU+XN0w1lB71ZQ7or4M/SXJQy+jzs37+XJreF0vbKpdWWAZyqZ2HI6J+HS2kuSjWkeajobjKbCUO2qqF8AMwa0of6z9JclDL6PCgMWTP65+FSmotSDWkebPbWehEREZG6pDAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIobWqK47ICJyJRcvXiQz80CttJ2VdYgmtdKyiNQXCkMiYvMyMw9w+rZQAmqh7Z9qoU0RqV8UhkSkXggA2tZCuwdroU0RqV+0Z0hEREQMTWFIREREDE1hSERERAxNYUhEREQMTWFIREREDE1hSERERAxNYUhEREQMTWFIREREDE1hSERERAxNYUhEREQMTWFIREREDE1hSERERAxNYUhEREQMrc6/tT44OPiKNYsXL6Zbt26Wf09KSmLRokVkZmbi4eFB3759iYmJwdXVtcxrk5OTmTt3LhkZGbi4uBAeHk5sbCxNmzat0XGIiIhI/VTnYWjMmDHlHs/JyWH58uU0bdqUNm3aWI7Pnz+fGTNmEBwczPDhw8nIyGDRokXs2rWLxYsX4+TkZKldt24dsbGx+Pn5MWTIEI4dO0ZiYiIpKSmsWbMGDw+PWh+fiIiI2LY6D0Njx44t9/jo0aMBeO211/Dx8QHg6NGjzJo1i5CQEJYsWYKjoyMAcXFxzJkzh5UrVzJ8+HAACgoKmDp1Kn5+fiQlJeHm5gZAjx49mDx5MnPnzmXixIm1PTwRERGxcTa5ZyghIYEtW7YwYMAA7rzzTsvxFStWYDKZiI6OtgQhKA1Obm5urFq1ynJs/fr15OXlMWrUKEsQAhg4cCABAQEkJCRw8eLFazMgERERsVk2F4bOnTvHzJkzue666xg/frzVuZSUFAC6du1qddzZ2ZlOnTqRnp7OmTNnrGov3WtkFhYWRl5eHnv37q2NIYiIiEg9YnNh6P333+e3335j1KhRZTY5Z2Vl0axZM6uVHjNfX18ADh48CEB2djYAfn5+ZWpbtWplVSsiIiLGZVNhqKioiA8++ABnZ2dGjBhR5nxeXh7u7u7lvtZ8PD8/H4Dc3FycnJxwcXEpU2sOU+ZaERERMa4630B9qY0bN3LixAkiIyNp0qRJmfMmk8nqbrFLmY8XFhZWu7Yy3t7X0aiRQ5X6X1/4+JQfKI1Ic1HK1uchN7fsanB90aSJm83P7+XqW39rk+aiVEOfB5sKQx9++CEAgwYNKve8i4sLFy5cKPdcUVERAI0bN652bWVyc89esaY+8fFx58SJM3XdDZuguShVH+bh1Kl8yv71qH44dSrf5uf3UvXh83CtaC6Qcwx7AAAgAElEQVRKNaR5qCjU2cxlsvz8fLZv346vry8dOnQot8bDw8OyQfpy5uPmy2UeHh4UFhZags/l73VprYiIiBiXzYShr7/+mgsXLnDfffdVWOPv709OTg7nz58vc+7IkSPY29vTunVrSy3A4cOHy9SajwUEBNRAz0VERKQ+s5kwlJaWBkCXLl0qrAkNDaW4uJjU1FSr44WFhaSlpREUFGTZHB0aGgr8cYv9pbZt24a7uzuBgYE11X0RERGpp2wmDP3yyy8AFV4iA+jfvz8ODg7Mnj3b6vLXvHnzyM/PJzIy0nKsd+/euLq6Eh8fT15enuX46tWryczMZNCgQdjb28zwRUREpI7YzAbqrKwsXFxcaN68eYU1bdq0ISoqigULFhAREUF4eDj79u0jOTmZzp07M3jwYEutl5cXEyZMYMqUKURERNC3b1+OHz/Oxo0b8ff3Jzo6+loMS0RERGyczYShvLw8brjhhivWxcbG0qJFC5YtW8bixYvx8fFh1KhRjBkzpsyt9EOGDMHT05P4+HiWLl2Kp6cnERERjBs3Di8vr9oaioiIiNQjNhOGdu7cWaU6Ozs7hg0bxrBhw6pU369fP/r16/dnuiYiIiINmDbNiIiIiKEpDImIiIihKQyJiIiIoSkMiYiIiKEpDImIiIihKQyJiIiIoSkMiYiIiKEpDImIiIihKQyJiIiIoSkMiYiIiKEpDImIiIihKQyJiIiIoSkMiYiIiKEpDImIiIihKQyJiIiIoSkMiYiIiKEpDImIiIihKQyJiIiIoSkMiYiIiKEpDImIiIihKQyJiIiIoSkMiYiIiKEpDImIiIihKQyJiIiIoSkMiYiIiKEpDImIiIihKQyJiIiIoSkMiYiIiKEpDImIiIihKQyJiIiIoSkMiYiIiKEpDImIiIihKQyJiIiIoSkMiYiIiKEpDImIiIihKQyJiIiIodlUGProo48YOHAgt956K3fccQcxMTEcPHiwTF1SUhIRERF06tSJu+66i+nTp1NQUFBum8nJyURGRhISEsJtt93GpEmTyMnJqe2hiIiISD1hM2Fo5syZTJgwgTNnzjB06FDCwsL49NNPiYyM5PDhw5a6+fPnM3HiRIqLixk+fDjt2rVj0aJFPP744xQVFVm1uW7dOqKjo8nJyWHIkCF0796dxMREHn30UX7//fdrPUQRERGxQY3qugMAu3fvZv78+YSFhbFgwQJcXFwAuO+++3jmmWd4++23mT59OkePHmXWrFmEhISwZMkSHB0dAYiLi2POnDmsXLmS4cOHA1BQUMDUqVPx8/MjKSkJNzc3AHr06MHkyZOZO3cuEydOrJsBi4iIiM2wiZWhpUuXAvDyyy9bghDA/fffT2RkJDfeeCMAK1aswGQyER0dbQlCAKNHj8bNzY1Vq1ZZjq1fv568vDxGjRplCUIAAwcOJCAggISEBC5evFjbQxMREREbZxNh6IsvvqBt27YEBASUOffyyy/z5JNPApCSkgJA165drWqcnZ3p1KkT6enpnDlzxqq2W7duZdoMCwsjLy+PvXv31ug4REREpP6p8zCUk5PDqVOnuOmmm9i/fz9jxoyhS5cuhIaGEhMTQ3Z2tqU2KyuLZs2aWa30mPn6+gJYNlybX+fn51emtlWrVla1IiIiYlx1HoZ+++03AI4fP86gQYM4cuQIjzzyCKGhoWzatInIyEiOHDkCQF5eHu7u7uW2Yz6en58PQG5uLk5OTlaX3czMYcpcKyIiIsZV5xuoz549C5Re1nrooYeYPn06Dg4OACxZsoRXXnmFadOm8fbbb2MymXByciq3HfPxwsJCgGrVVsbb+zoaNXKo3qBsnI9P+YHSiDQXpWx9HnJzy64G1xdNmrjZ/Pxerr71tzZpLko19Hmo8zBkb1+6OOXg4MCkSZMsQQhg2LBhvP/++2zdupVz587h4uLChQsXym3HfFt948aNAapVW5nc3LNVH0w94OPjzokTZ+q6GzZBc1GqPszDqVP5NKnrTlylU6fybX5+L1UfPg/XiuaiVEOah4pCXZ1fJjNf3vL19cXLy8vqnL29PcHBwVy4cIGjR4/i4eFh2SB9OfNxc3seHh4UFhaWefYQ/HF5rKJLbiIiImIcdR6G/Pz8cHBwqHAVx2QyAaWrOP7+/uTk5HD+/PkydUeOHMHe3p7WrVsD4O/vD2D1wEYz87Hy7l4TERERY6nzMOTs7Ez79u05duwYmZmZVudMJhPp6el4eXnRvHlzQkNDKS4uJjU11aqusLCQtLQ0goKCLJujQ0NDgT9usb/Utm3bcHd3JzAwsHYGJSIiIvVGnYchgMGDBwPw6quvWq0Qvfvuu/z6669ERETg4OBA//79cXBwYPbs2VaXv+bNm0d+fj6RkZGWY71798bV1ZX4+Hjy8vIsx1evXk1mZiaDBg2y7FcSERER46rzDdQAjzzyCFu2bOHTTz8lIiKCu+66i/3797N161b8/f0ZM2YMAG3atCEqKooFCxYQERFBeHg4+/btIzk5mc6dO1tCFYCXlxcTJkxgypQpRERE0LdvX44fP87GjRvx9/cnOjq6roYrIiIiNsQmwpCdnR1xcXF88MEHrFq1ig8++AAvLy+GDBnCM888Y7XROTY2lhYtWrBs2TIWL16Mj48Po0aNYsyYMWVupR8yZAienp7Ex8ezdOlSPD09iYiIYNy4cWU2a4uIiIgx2ZWUlJTUdSdsWUO5ndCsId0i+WdpLkrVh3nYv38vTW4LpW0ttL0JCIBaaTsDOPXtDgIDb6qF1mtHffg8XCuai1INaR5s9tZ6ERERkbqkMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihqYwJCIiIoamMCQiIiKGpjAkIiIihtaorjtgNnPmTObNm1fuuX79+jFz5kzLvyclJbFo0SIyMzPx8PCgb9++xMTE4OrqWua1ycnJzJ07l4yMDFxcXAgPDyc2NpamTZvW2lhERESk/rCZMLRnzx6cnJx44oknypy76aabLH+eP38+M2bMIDg4mOHDh5ORkcGiRYvYtWsXixcvxsnJyVK7bt06YmNj8fPzY8iQIRw7dozExERSUlJYs2YNHh4e12RsIiIiYrtsJgxlZGQQFBTE2LFjK6w5evQos2bNIiQkhCVLluDo6AhAXFwcc+bMYeXKlQwfPhyAgoICpk6dip+fH0lJSbi5uQHQo0cPJk+ezNy5c5k4cWLtD0xERERsmk3sGcrPz+fIkSMEBwdXWrdixQpMJhPR0dGWIAQwevRo3NzcWLVqleXY+vXrycvLY9SoUZYgBDBw4EACAgJISEjg4sWLNT8YERERqVdsIgylp6cDXDEMpaSkANC1a1er487OznTq1In09HTOnDljVdutW7cy7YSFhZGXl8fevXv/dN9FRESkfrOJMLRnzx4AcnNzeeyxx+jatStdu3YlJiaGAwcOWOqysrJo1qyZ1UqPma+vLwAHDx4EIDs7GwA/P78yta1atbKqFREREeOyqTC0cOFC3NzcGDRoEB07dmTTpk0MHjyYX375BYC8vDzc3d3LbcN8PD8/HygNVk5OTri4uJSpNYcpc62IiIgYl01soHZwcMDX15fp06dbXdb66KOPmDBhApMmTSIxMRGTyWR1t9ilzMcLCwsBqlUrIiIixmUTYeill14q9/hf//pXVq5cSUpKCgcOHMDFxYULFy6UW1tUVARA48aNAapVWxlv7+to1MjhinX1iY9P+atrRqS5KGXr85CbW/bSeH3RpImbzc/v5epbf2uT5qJUQ58HmwhDlbn55ptJSUnh8OHDeHh4WDZIX8583Hy5zMPDg8LCQoqKisqsEJkvj1V0ye1Subln/0z3bY6PjzsnTpQ/h0ajuShVH+bh1Kl8mtR1J67SqVP5Nj+/l6oPn4drRXNRqiHNQ0Whrs73DJlMJnbv3s2uXbvKPX/+/Hmg9I4xf39/cnJyLMcudeTIEezt7WndujUA/v7+ABw+fLhMrflYQEBATQxBRERE6rE6D0PFxcUMHTqUv//972We+1NSUsL3339Po0aN+Mtf/kJoaCjFxcWkpqZa1RUWFpKWlkZQUJBlc3RoaCjwxy32l9q2bRvu7u4EBgbW0qhERESkvqjzMOTk5ER4eDinT5/mnXfesTr37rvvkpGRwYMPPoiHhwf9+/fHwcGB2bNnW/b9AMybN4/8/HwiIyMtx3r37o2rqyvx8fHk5eVZjq9evZrMzEwGDRqEvX2dD19ERETqmE3sGZo4cSLff/89b775Jtu3b6ddu3b8+OOPbN++ncDAQJ577jkA2rRpQ1RUFAsWLCAiIoLw8HD27dtHcnIynTt3ZvDgwZY2vby8mDBhAlOmTCEiIoK+ffty/PhxNm7ciL+/P9HR0XU1XBEREbEhNhGGWrVqxZo1a4iLi+OLL74gJSWF66+/nqioKJ566imrjc6xsbG0aNGCZcuWsXjxYnx8fBg1ahRjxowps1F6yJAheHp6Eh8fz9KlS/H09CQiIoJx48bh5eV1rYcpIiIiNsgmwhBA8+bNmTZt2hXr7OzsGDZsGMOGDatSu/369aNfv35/tnsiIiLSQGnTjIiIiBiawpCIiIgYmsKQiIiIGJrCkIiIiBiawpCIiIgYmsKQiIiIGJrCkIiIiBiawpCIiIgYmsKQiIiIGJrCkIiIiBhatcJQSkoKBw4cuGLd999/z8qVK6+6UyIiIiLXSrXC0IgRI5g/f/4V69577z3+/e9/X3WnRERERK6VSr+ode3atVy8eNHqWFZWFklJSRW+5syZM2zbtg17e12BExEREdtXaRjavXs3S5Yswc7ODij9xvi0tDTS0tIqbbSkpISHHnqo5nopIiIiUksqDUPPPPMM58+fp6SkBIDVq1fTunVrunbtWm69nZ0dzs7OtG7dmsGDB9d8b0VERERqWKVhyM3NjalTp1r+ffXq1dx666288sortd4xERERkWuh0jB0ufT09Nrqh4iIiEidqFYYutTp06c5d+4cxcXFFda0bNnyapsXERERuSaqHYaWLl3K4sWLycrKqrTOzs6On3/++ao7JiIiInItVCsMrVq1yrKHqHHjxnh7e9Oo0VUvLomIiIjUuWolmWXLlmFvb8/LL7/MgAED9CwhERERqfeqFYYOHDhA586dGThwYG31R0REROSaqtbSjqurK25ubrXVFxEREZFrrlphqEePHuzatYv8/Pza6o+IiIjINVWtMBQbG4uDgwOxsbFkZ2fXVp9ERERErplq7RmaPn06rVq14osvvuC+++7Dw8MDT09Py3eXXW7Tpk010kkRERGR2lKtMHR5uDl9+jSnT58ut7aigCQiIiJiS6oVhj777LPa6oeIiIhInahWGPL19a2tfoiIiIjUCT01UURERAytWitDI0eOrHKtnZ0d77//frU7JCIiInItVSsMbd++/Yo1dnZ2lJSUaAO1iIiI1AvVCkNLly4t93hxcTG///47aWlpLFu2jHvvvZd//etfNdJBERERkdpUrTAUGhpa6flevXrRs2dPhg0bRocOHRg2bNif6pyIiIhIbavxDdQhISF07tyZ5cuX13TTIiIiIjWuVu4m8/LyIisrqzaaFhEREalRNR6Gjh8/zrZt2/D29q7ppkVERERqXLX2DM2bN6/CcyaTiZMnT7J582by8/N56KGHrrpTr732Gu+++y6LFy+mW7duVueSkpJYtGgRmZmZeHh40LdvX2JiYnB1dS3TTnJyMnPnziUjIwMXFxfCw8OJjY2ladOmV903ERERaViqFYbefPPNCm+ZLykpsfz55ptvJiYm5qo6tHv37gqfTzR//nxmzJhBcHAww4cPJyMjg0WLFrFr1y4WL16Mk5OTpXbdunXExsbi5+fHkCFDOHbsGImJiaSkpLBmzRo8PDyuqn8iIiLSsFQrDD399NMVhiE7OztcXV1p27Yt3bt3x96++lfgioqKmDx5MhcvXixz7ujRo8yaNYuQkBCWLFmCo6MjAHFxccyZM4eVK1cyfPhwAAoKCpg6dSp+fn4kJSXh5uYGQI8ePZg8eTJz585l4sSJ1e6fiIiINDzVCkNjx46trX4ApZfhDh48yO23384333xjdW7FihWYTCaio6MtQQhg9OjRLF68mFWrVlnC0Pr168nLy2Ps2LGWIAQwcOBA4uPjSUhIYPz48Tg4ONTqeERERMT2XfUG6qKiInbu3MnmzZtJTk7mhx9+wGQyXXVH0tPTeeedd4iOjiYoKKjM+ZSUFAC6du1qddzZ2ZlOnTqRnp7OmTNnrGov328EEBYWRl5eHnv37r3qvoqIiEjDUa2VISjdKD1r1iw++OADzp07Z3XO3d2dyMhIYmJirFZvruTixYtMmjSJ1q1bEx0dzX//+98yNVlZWTRr1sxqpcfM19cXgIMHD9KxY0eys7MB8PPzK1PbqlUrS227du2q3EcRERFpmKoVhi5evMiTTz7JV199hb29Pbfeeiu+vr4UFxeTnZ3Nzz//THx8POnp6SxYsKDK7S5cuJBffvmFZcuWWW2CvlReXp4lyFzO3d0dgPz8fAByc3NxcnLCxcWlTK05TJlrRURExNiqFYZWrFjBl19+SYcOHZgxY0aZlZesrCyeffZZvvrqK9asWcMjjzxyxTYPHjzI7NmzGTp0KCEhIRXWmUymCoOS+XhhYWG1a6/E2/s6GjVqWHuLfHzc67oLNkNzUcrW5yE3t+yKcH3RpImbzc/v5epbf2uT5qJUQ5+HaoWhxMREXF1dmT9/Pk2aNClz/sYbb+Sdd97h3nvvZfXq1VcMQyUlJUyePJmmTZvy7LPPVlrr4uLChQsXyj1XVFQEQOPGjatdeyW5uWerVFdf+Pi4c+LEmbruhk3QXJSqD/Nw6lQ+Zf+PUz+cOpVv8/N7qfrwebhWNBelGtI8VBTqqhWG9u3bR/fu3csNQmZNmjQhLCyMHTt2XLG9pUuXsmPHDt55551yH5p4KQ8PD8sG6cuZj5svl3l4eFBYWEhRUVGZFSLz5TFzrYiIiBhbtTdQV1VFKzOX2rRpEwBPPPFEuedHjhwJwGeffYa/vz8pKSmcP3++zF6gI0eOYG9vT+vWrQHw9/dn586dHD58mDZt2ljVHj58GICAgIDqDUhEREQapGqFoYCAAFJSUjh9+jSenp7l1uTl5ZGSklImhJTn4YcfJiwsrMzxL7/8kl27dvHwww/j6+uLh4cHoaGhbNu2jdTUVO644w5LbWFhIWlpaQQFBVk2R4eGhpKQkFBuP7Zt24a7uzuBgYHVGbqIiIg0UNUKQwMGDOCVV17hqaeeYsaMGTRv3tzq/K+//sqzzz5LQUFBlb6bbMCAAeUe//333y1hyPysoP79+zN//nxmz55NWFiY5fLXvHnzyM/PJzIy0vL63r17M23aNOLj4+nTpw9eXl4ArF69mszMTKKioq7qCdkiIiLS8FQrDA0ZMoRNmzaRkpJCr1696NSpE76+vtjZ2ZGdnc2uXbswmUx07dqVoUOH1mhH27RpQ1RUFAsWLCAiIoLw8HD27dtHcnIynTt3ZvDgwZZaLy8vJkyYwJQpU4iIiKBv374cP36cjRs34u/vT3R0dI32TUREROqvaoUhBwcHFi5cyBtvvMGKFStITU0lNTXVcr5x48YMGzaMZ599lkaNan47UmxsLC1atGDZsmUsXrwYHx8fRo0axZgxY8pslB4yZAienp7Ex8ezdOlSPD09iYiIYNy4cZaVIhERERG7kku/br4aCgsL+eGHH/jtt98AuP7662nfvn25DzqszxrK7YRmDekWyT9Lc1GqPszD/v17aXJbKG1roe1NQADUStsZwKlvdxAYeFMttF476sPn4VrRXJRqSPNQ0a31Vd448/nnn7NhwwbLvzs7O9OlSxf69etH06ZNmTp1Kh999BHFxcV/vrciIiIi18gVw9DZs2cZMWIETz/9NKtXry63Ztu2bezZs4eXXnqJYcOGkZeXV+MdFREREakNlYah4uJiHn/8cVJSUmjSpInVLe2XGjRoEP/3f/9H06ZN+f7773n66adrpbMiIiIiNa3SMLRmzRq+//57OnbsyLp164iKiiq3rkWLFowePZrVq1fTtm1bdu7cyUcffVQrHRYRERGpSZWGoXXr1tGoUSPeeOMNvL29r9jYDTfcwIwZMwD48MMPa6aHIiIiIrWo0jCUnp5O+/bty3w7fWWCgoLo0KEDP//885/unIiIiEhtqzQMnT17tsxTpqvC19e3wi9VFREREbEllYYhHx8fy3OEquPkyZM0btz4qjslIiIicq1UGoYCAwPJyMio1ipPfn4+P/30E/7+/n+2byIiIiK1rtIw1L9/fwoKCpg/f36VG5w/fz7nzp3jzjvv/NOdExEREaltlYah+++/n4CAABYuXMjs2bO5cOFChbUmk4k5c+awYMECPDw8GD58eI13VkRERKSmVfptqk5OTrz55ps8+uijvP3226xevZrevXvToUMHmjVrhslk4tSpU+zevZutW7dy7NgxnJ2dmTdvHk2aNLlWYxARERG5alf8avng4GASExMZP348P/74I0uXLi1TY/6u1y5duvDiiy/Stm1tfOWhiIiISM27YhgC8Pf3Z/Xq1aSmprJx40YOHDjAiRMncHBwwMfHh1tuuYVevXrRsWPH2u6viIiISI2qUhgy69KlC126dKmtvoiIiIhcc1f81noRERGRhkxhSERERAxNYUhEREQMTWFIREREDE1hSERERAytWneTiYhU5uLFi2RmHqjxdrOyDqHHuIpIbVEYEpEak5l5gNO3hRJQw+3+VMPtiYhcSmFIRGpUAFDTz6A/WMPtiYhcSmFIRKSWXKT0El9t8fdvg4ODQ621L2IUCkMiIrUkC/CMHFAr+50OApnf7iAw8KZaaF3EWBSGRERqUW1cNjQ7VUvtihiNbq0XERERQ1MYEhEREUNTGBIRERFDUxgSERERQ1MYEhEREUNTGBIRERFDUxgSERERQ1MYEhEREUNTGBIRERFDUxgSERERQ7OZMJSbm8srr7xC79696dixI/369SM+Ph6TyVSmNikpiYiICDp16sRdd93F9OnTKSgoKLfd5ORkIiMjCQkJ4bbbbmPSpEnk5OTU9nBERESknrCJMJSfn8/QoUNZsmQJQUFBDBs2DHd3d/773/8yZswYSkpKLLXz589n4sSJFBcXM3z4cNq1a8eiRYt4/PHHKSoqsmp33bp1REdHk5OTw5AhQ+jevTuJiYk8+uij/P7779d6mCIiImKDbOKLWt955x0OHDjA5MmTGTlypOV4bGws69atY+vWrdxzzz0cPXqUWbNmERISwpIlS3B0dAQgLi6OOXPmsHLlSoYPHw5AQUEBU6dOxc/Pj6SkJNzc3ADo0aMHkydPZu7cuUycOPHaD1ZERERsik2sDB05coQWLVowdOhQq+P9+vUD4PvvvwdgxYoVmEwmoqOjLUEIYPTo0bi5ubFq1SrLsfXr15OXl8eoUaMsQQhg4MCBBAQEkJCQwMWLF2tzWCIiIlIP2EQYeuONN0hOTqZRI+uFqgMHDgDQrFkzAFJSUgDo2rWrVZ2zszOdOnUiPT2dM2fOWNV269atzPuFhYWRl5fH3r17a3YgIiIiUu/YRBi6VElJCTk5OSxdupS33nqLli1b8te//hWArKwsmjVrZrXSY+br6wvAwYMHAcjOzgbAz8+vTG2rVq2sakVERMS4bGLP0KXi4uKYO3cuULoitHDhQjw9PQHIy8uzBJnLubu7A6WbsaH07jQnJydcXFzK1JrDlLlWREREjMvmVoZ8fX2Jiori3nvv5dSpUwwbNoyffvoJAJPJhJOTU7mvMx8vLCysdq2IiIgYl82tDA0aNMjy5+TkZEaPHs3EiRNZu3YtLi4uXLhwodzXmW+rb9y4MUC1aivj7X0djRo5VGsMts7Hx72uu2AzNBelamoecnPLXsKW2tOkiVutfIb138UfNBelGvo82FwYutQ999zDbbfdxjfffENWVhYeHh6WDdKXMx83Xy7z8PCgsLCQoqKiMitE5stj5trK5Oae/TNDsDk+Pu6cOFH+HBqN5qJUTc7DqVP5NKmRlqQqTp3Kr/HPsP67+IPmolRDmoeKQl2dXyYzmUx88803fP311+Web9myJVC6B8jf35+cnBzOnz9fpu7IkSPY29vTunVrAPz9/QE4fPhwmVrzsYCAgJoYgoiIiNRjdR6GoPQ5QePHjy/3uT/p6enY2dnRqlUrQkNDKS4uJjU11aqmsLCQtLQ0goKCLJujQ0NDgT9usb/Utm3bcHd3JzAwsBZGIyIiIvVJnYehRo0aWTZLL1y40OrcsmXL+PHHH7nnnnto1qwZ/fv3x8HBgdmzZ1t99ca8efPIz88nMjLScqx37964uroSHx9PXl6e5fjq1avJzMxk0KBB2NvX+fBFRESkjtnEnqF//OMfpKam8sYbb7Bt2zbatm3LL7/8wrfffkurVq3417/+BUCbNm2IiopiwYIFREREEB4ezr59+0hOTqZz584MHjzY0qaXlxcTJkxgypQpRERE0LdvX44fP87GjRvx9/cnOjq6roYrIiIiNsQmwlDz5s1ZvXo1s2bNYsuWLXz33Xdcf/31/O1vf+PJJ5/E29vbUhsbG0uLFi1YtmwZixcvxsfHh1GjRjFmzJgyG6WHDBmCp6cn8fHxLF26FE9PT0yN/skAACAASURBVCIiIhg3bhxeXl7XepgiIiJig2wiDAH4+PgwderUK9bZ2dkxbNgwhg0bVqV2+/XrZ/mOMxEREZHLadOMiIiIGJrCkIiIiBiawpCIiIgYmsKQiIiIGJrCkIiIiBiawpCIiIgYmsKQiIiIGJrCkIiIiBiawpCIiIgYmsKQiIiIGJrCkIiIiBiawpCIiIgYmsKQiIiIGJrCkIiIiBiawpCIiIgYmsKQiIiIGJrCkIiIiBiawpCIiIgYmsKQiIiIGJrCkIiIiBiawpCIiIgYmsKQiIiIGJrCkIiIiBiawpCIiIgYmsKQiIiIGJrCkIiIiBiawpCIiIgYmsKQyP9v796joir3N4A/QHLRYQAVWYootwbrGCqTmFkWeEs6GaGIBqZlOlakx1t6NF2lnaxOy45k3k/eXQsxAiuts6ygU8cIvNCvOpQi4wgaIhcFxQFm9u8PzuwcZ1BUNszs/Xz+831fXt79XYDP7P3uvYmISNEYhoiIiEjRGIaIiIhI0RiGiIiISNEYhoiIiEjRGIaIiIhI0RiGiIiISNEYhoiIiEjRGIaIiIhI0RiGiIiISNHu6ugFWFRUVOD9999Hbm4uKisr4ePjg6FDh2LOnDkICgqyGpuVlYVt27ZBr9dDrVZj7NixmD17Nrp06WIzb05ODtavX4/ffvsNnp6eiImJwfz589GtW7f2OjQiIiJyYA5xZqiiogKJiYlIT09HWFgYpkyZgvvuuw+ffvopJkyYAL1eL47duHEjFi1aBLPZjJSUFPTr1w/btm3D9OnT0dDQYDXvp59+Cp1Oh8rKSkyePBkPPPAAPv74Y0yaNAmXLl1q56MkIiIiR+QQZ4bef/99nDt3DosXL8azzz4rtu/fvx8LFy7EW2+9hQ0bNuDs2bNIS0vDoEGDsHPnTnTq1AkAsGbNGqxbtw579+5FSkoKAODy5ctYuXIlgoKCkJWVBZVKBQAYNmwYli5divXr12PRokXtf7BERETkUBzizNChQ4fQtWtXTJ061ap93Lhx6NOnD7799luYzWakp6ejqakJOp1ODEIAMGvWLKhUKmRkZIhtn332GWpqajBt2jQxCAHAhAkTEBISgszMTJhMJukPjoiIiBxah4chk8kEnU6H1NRUuLraLsfd3R2NjY1obGxEfn4+AGDw4MFWYzw8PDBw4EAUFRWhtrYWAMSxQ4YMsZkzOjoaNTU1OHHiRFsfDhERETmZDr9M5ubmZnNGyKK4uBinTp1Cnz594OHhAYPBgO7du1ud6bEIDAwEAJSUlCAyMhJnzpwBAJvN1wDQu3dvcWy/fv3a6lCIiIjICXX4maGWmM1mrFy5EmazGRMnTgQA1NTUwNvb2+54S3tdXR0AoLq6Gu7u7vD09LQZawlTlrFERESkXB1+ZsgeQRCwfPlyHD58GP379xfPHDU1NcHd3d3u11jajUbjLY+9ET+/zrjrLrdbPgZH5u9vP1AqEWvRrK3qUF1te9aWpNO1q0qSn2H+XvyBtWgm9zo4XBhqamrCsmXLkJmZiaCgIKxbt04ML56enmhsbLT7dZbb6r28vG557I1UV1+55WNwZP7+3qioqO3oZTgE1qJZW9ahqqoOXdtkJmqNqqq6Nv8Z5u/FH1iLZnKqQ0uhzqHCUH19PebMmYPc3FwEBwdj69atCAgIEPvVarW4Qfp6lnbL5TK1Wg2j0YiGhgabM0SWy2MtXXIjIiIi5XCYPUMXL17E1KlTkZubi3vvvRd79uxBr169rMYEBwejsrISV69etfn6srIyuLq6om/fvuJYACgtLbUZa2kLCQlp46MgIiIiZ+MQYchoNEKn06GwsBDR0dHYuXOn3ddlaLVamM1mFBQU2Hz98ePHER4eLm6O1mq1AP64xf5aeXl58Pb2RlhYmARHQ0RERM7EIcLQ6tWrcezYMQwaNAibN2+2e+s8ADzxxBNwc3PD2rVrrV69sWHDBtTV1SEpKUlsGzlyJLp06YItW7agpqZGbN+3bx/0ej0SExPtPteIiIiIlKXD9wxVVFRg9+7dAIDQ0FBs3rzZ7riZM2ciNDQUzz33HDZv3oz4+HjExMTg5MmTyMnJQVRUlHgLPgD4+vpi4cKFeO211xAfH4+xY8eivLwcBw8eRHBwMHQ6XbscHxERETm2Dg9DhYWF4l1fH330UYvjpk6dCg8PD8yfPx89e/bEnj17sGPHDvj7+2PatGlITU212Sg9efJk+Pj4YMuWLdi9ezd8fHwQHx+PuXPnwtfXV9LjIiIiIufQ4WFo5MiR+PXXX1s93sXFBcnJyUhOTm7V+Li4OMTFxd3u8oiIiEjmuGmGiIiIFI1hiIiIiBSNYYiIiIgUrcP3DBER0a0zATAYTrf5vNXVKqjVPeDmJq93MhLdCMMQEZETMgDwSUpo83fBlQDwOXwEYWF3t/HMRI6LYYiIyEmFANBIMG+VBHMSOTLuGSIiIiJFYxgiIiIiRWMYIiIiIkVjGCIiIiJFYxgiIiIiRWMYIiIiIkVjGCIiIiJFYxgiIiIiReNDF4kUxmQyQa8/Jf67ulqFqqq6NpnbYDjd5k9EJiKSGsMQkcLo9adwcagWIde0tVWA+bmN5iEiak8MQ0QKJNVrHEokmJOISGrcM0RERESKxjBEREREisYwRERERIrGMERERESKxjBEREREisYwRERERIrGMERERESKxjBEREREisYwRERERIrGMERERESKxjBEREREisYwRERERIrGMERERESKxjBEREREisYwRERERIrGMERERESKxjBEREREisYwRERERIrGMERERESK5nBhqLy8HFqtFtu2bbPbn5WVhfj4eAwcOBDDhw/HqlWrcPnyZbtjc3JykJSUhEGDBmHo0KFYsmQJKisrJVw9ERERORuHCkOXL1/Gyy+/jLq6Orv9GzduxKJFi2A2m5GSkoJ+/fph27ZtmD59OhoaGqzGfvrpp9DpdKisrMTkyZPxwAMP4OOPP8akSZNw6dKl9jgcIiIicgJ3dfQCLMrKyvDyyy/j559/ttt/9uxZpKWlYdCgQdi5cyc6deoEAFizZg3WrVuHvXv3IiUlBUBzqFq5ciWCgoKQlZUFlUoFABg2bBiWLl2K9evXY9GiRe1zYEREROTQHOLM0LZt2/DEE0+gqKgIDzzwgN0x6enpaGpqgk6nE4MQAMyaNQsqlQoZGRli22effYaamhpMmzZNDEIAMGHCBISEhCAzMxMmk0m6AyIiIiKn4RBhaMeOHQgMDMSuXbvw5JNP2h2Tn58PABg8eLBVu4eHBwYOHIiioiLU1tZajR0yZIjNPNHR0aipqcGJEyfa8hCIiIjISTlEGHr99deRlZWFqKioFscYDAZ0797d6kyPRWBgIACgpKQEAHDmzBkAQFBQkM3Y3r17W40lIiIiZXOIMPTwww/Dzc3thmNqamrg7e1tt8/Sbtl4XV1dDXd3d3h6etqMtYSpljZpExERkbI4zAbqm2lqaoK7u7vdPku70Wi85bE34+fXGXfddeOg5mz8/e2HSiVSYi2qq23PrhJdq2tXlSJ/N+xhHZrJvQ5OE4Y8PT3R2Nhot89yW72Xl9ctj72Z6uort7pUh+bv742KitqOXoZDUGotqqrq0LWjF0EOraqqTpG/G9dT6t+I68mpDi2FOoe4TNYaarVa3CB9PUu75XKZWq2G0Wi0efYQ8MflsZYuuREREZGyOE0YCg4ORmVlJa5evWrTV1ZWBldXV/Tt21ccCwClpaU2Yy1tISEh0i2WiIiInIbThCGtVguz2YyCggKrdqPRiOPHjyM8PFzcHK3VagH8cYv9tfLy8uDt7Y2wsDDpF01EREQOz2nC0BNPPAE3NzesXbvW6vLXhg0bUFdXh6SkJLFt5MiR6NKlC7Zs2YKamhqxfd++fdDr9UhMTISrq9McOhEREUnIaTZQh4aG4rnnnsPmzZsRHx+PmJgYnDx5Ejk5OYiKisLEiRPFsb6+vli4cCFee+01xMfHY+zYsSgvL8fBgwcRHBwMnU7XgUdCREREjsRpwhAAzJ8/Hz179sSePXuwY8cO+Pv7Y9q0aUhNTbW5lX7y5Mnw8fHBli1bsHv3bvj4+CA+Ph5z586Fr69vBx0BERERORqHC0MJCQlISEiw2+fi4oLk5GQkJye3aq64uDjExcW15fKIiIhIZrhxhoiIiBSNYYiIiIgUzeEukxERUccxATAYTks2f3Bw6E3fRUnU3hiGiIhIZADgk5QgyStbSgDoDx9BWNjdEsxOdPsYhoiIyEoIAI1Ec1dJNC/RnWAYInJAJpMJev0pSeY2GE7zRa1ERNdgGCJyQHr9KVwcqoUUb9D7WYI5iYicGcMQkYOS6lJFiQRzEhE5M95aT0RERIrGMERERESKxjBEREREisYwRERERIrGMERERESKxjBEREREisYwRERERIrGMERERESKxjBEREREisYwRERERIrGMERERESKxjBEREREisYwRERERIrGt9YT3SaTyQS9/pQkcxsMp9FVkpmJiOh6DENEt0mvP4WLQ7UIkWDunyWYk4iI7GMYIroDIQA0EsxbIsGcRB3NhOaznlIJDg6Fm5ubZPOTfDEMERFRuzAA8ElKkOQScAkA/eEjCAu7W4LZSe4YhoiIqN1IdTYVAKokmpfkj3eTERERkaIxDBEREZGiMQwRERGRojEMERERkaIxDBEREZGiMQwRERGRojEMERERkaIxDBEREZGi8aGLJGs3eplqdbUKVVV1tz03X6ZKRCQPDEMkazd7meqdhBm+TJXIcUjx3rNrPzDxvWfyxjBEsseXqRLJn1TvPesKvvdMCWQfhpqamrBr1y7s3bsXpaWl8Pf3R0JCAmbOnIlOnTp19PLof250OetO8FIWkXJI9cHHBOD/2vis07V41qnjyT4MrVixAunp6dBqtYiNjcXRo0eRlpaGX3/9FWlpaR29PPqfm13Oul28lEVEd0qqs04Azzo5ClmHoaNHjyI9PR1jxozBmjVr4OLiAkEQsHjxYmRlZeHrr79GTExMRy+T/keKT3W8lEVEbUGqs04AUCXRvNR6sg5Du3fvBgCkpqbCxcUFAODi4oJ58+YhOzsbGRkZHRKGpLokZMFTrkRERK0n6zBUUFAAPz8/aDTWeT4gIADBwcHIz8/vkHVJdUkIAE4CMKRnok+fvnb77/R2cgYtIiKSG9mGoYaGBvz+++8YMGCA3f7AwECUlJSgqqoKXbu2/xZbKe9wutm17ds92psFrTvBjc5EpERSPBIAaL4CAbjAze3On618/Yfotpzbno740C3bMFRTUwMA8Pb2tttvaa+tre2QMCTVXpZSAL0lmvscgKakBHSWYO4iAD4SzFsqwZycu/3ndsY1O+vczrhmZ577P5Dm7+pRAAEAAttgrgsSzn29MgDogA3lsg1DTU1NAAB3d3e7/ZZ2o9F4w3n8/e2HqTvh7x8FCEKbzwtIt8FP6rn/LNG8zloPzt0+83Lu9puXc7fv3FL9TZV67o4i23eTeXp6AgAaGxvt9jc0NAAAvLy82m1NRERE5HhkG4ZUKhVcXV1RV2d/s3BtbS2Ali+jERERkTLINgy5u7ujV69eKC21f7W3tLQUfn5+8PX1beeVERERkSORbRgCAK1Wi4qKCpSUWG9XLi8vx+nTpzFw4MAOWhkRERE5ClmHofj4eADAe++9B7PZDAAQBAGrV6+GIAhISkrqyOURERGRA3ARBIlua3IQc+fOxYEDBxAZGYkhQ4bg2LFjKCgosHpFBxERESmXrM8MAcA777yD2bNno7q6Gtu3b8eFCxcwe/ZsvPvuu4oIQpcuXcJbb72FmJgYREZG4rHHHsMHH3xg95ECNTU1WLFiBWJjYzFgwAAkJCTgwIEDHbBq6V2+fBmxsbGIjY212y/nWvznP//Bs88+i/vvvx/33Xcf4uLisGnTJvFxFNeScx2A5kdwbNu2DXFxcYiMjMSIESPwwQcftHgXqhxUVFRg+fLleOSRR9C/f38MGzYMCxYswJkzZ2zGZmVlIT4+HgMHDsTw4cOxatUqXL58uQNWLb23334bERERyMvLs+lTQh3279+PCRMmYMCAAXjooYcwe/Zsmy0mgHxrIfszQ0p26dIlPP300zh58iQeeeQRhIaGIi8vDz///DNGjRqFtWvXimOvXLmClJQU/PLLLxg7dix69uyJf/3rXzhz5gyWLVuGlJSUDjyStrdy5Urs2rULgYGB+Oqrr6z65FyL7OxsLFq0CF26dMHo0aOhUqnw3Xffobi4GLGxsVi3bp34IUHOdbBYvnw50tPTodVqERUVhaNHj+LIkSMYM2YM0tLSOnp5ba6iogKJiYk4d+4chg0bhoiICJSUlCAnJwc+Pj5IT09HcHAwAGDjxo1YvXo1IiIiMHz4cPz222/Izc3FoEGDsGPHjhaf4eaMfvzxR0yaNAkmkwk7duzAkCFDxD4l1OG9997Dhg0bEBwcjNjYWJSXl+Pzzz+HSqVCZmYmevdufpSvrGshkGy9/vrrgkajEXbt2iW2mUwmQafTCRqNRvjhhx/E9vXr19uMra2tFR5//HFhwIABwoULF9p17VIqKCgQIiIiBI1GI8TExNj0y7UW9fX1wuDBgwWtVisYDAaxvaGhQXj++ecFjUYjfPHFF2K7XOtgceTIEUGj0Qgvv/yyYDabBUEQBLPZLLzyyiuCRqMRvvrqqw5eYdtbtmyZoNFohA8//NCqPTs7W9BoNIJOpxMEQRDKysqEe++9V0hKShIaGhrEcf/4xz8EjUYj7Ny5s13XLSWj0Sj8+c9/FjQajaDRaITvv/9e7FNCHQoLC4WIiAghJSVFqK+vF9sPHjwoaDQaYfHixYIgyL8Wsr9MplRXrlxBZmYmoqKikJycLLa7urrixRdfREJCgtVlkT179qB79+6YNGmS2KZSqTBr1izU19fjk08+adf1S8VoNGLp0qWIioqCSqWyO0autcjLy8PFixeRmJiIoKAgsb1Tp07Q6XQAgG+++UZsl2sdLHbv3g0ASE1NFc+Gubi4YN68eXBxcUFGRkZHLk8Shw4dQteuXTF16lSr9nHjxqFPnz749ttvYTabkZ6ejqamJuh0OnTq1EkcN2vWLKhUKlnVZsOGDSgpKcGDDz5o06eEOlh+D1asWCE+rBgAHnvsMSQlJaFPnz4A5F8LhiGZys/PR319PcaMGWPTFxkZiVWrVmHo0KEAAIPBgPLycmi1WpuX41lOF+fn50u/6HaQlpaGsrIyrFy50u6eMTnXIjAwEPPmzcOoUaNs+iynt69cuQJA3nWwKCgogJ+fHzQa6xciBAQEIDg42OmP73omkwk6nQ6pqalwdbX90+/u7o7GxkY0NjaKxz548GCrMR4eHhg4cCCKiorEB9c6s6KiImzatAk6nQ7h4eE2/UqowzfffAONRoOQkBCbvhUrVuCFF14AIP9aMAzJ1G+//QYACA8PFze8RUZGIjY2Fh988IHVWSGDwQAA4ieAa/n7+8PDwwN6vb5d1i2ln376CVu3bsULL7yAsLAwu2PkXIvw8HDodDpERUXZ9B06dEgcA8i7DkDz63h+//13u8cHNAfHS5cuoaqqqp1XJh03NzdMnTrV6kyxRXFxMU6dOoU+ffrAw8MDBoMB3bt3t3v2NDCw+fWc9jbXOhOTyYQlS5agb9++4pnR68m9DpWVlaiqqsLdd9+N4uJipKam4v7774dWq8Xs2bOtNtXLvRYMQzJ1/vx5AMCuXbvEX/iJEyeiU6dOSEtLw8KFC8WxNTU1AAC1Wm13LpVK5dSJH2h+R92SJUsQFhaGGTNmtDhOCbW4XnFxsbj58amnngIg/zpYjq+l1/FY2p35GFvLbDZj5cqVMJvNmDhxIoDm+tysNi296shZ/POf/8R///tfvPHGGy1u/JV7HSz/T5SXlyMxMRFlZWUYP348tFotvvjiCyQlJaGsrAyA/Gsh27fWy1VsbKz4w9mS5ORk8UW0OTk52LJlCx566CEAQH19PaZPn44DBw7g8ccfx8iRI8WzRC39QXB3d0d9fX0bHkXbaG0tli9fjo0bN+LEiRNIT0+3ut59PWesxa3U4Xq///47ZsyYgfr6evz1r39Fz549AThnHW5Fa44PgN1HUMiJIAhYvnw5Dh8+jP79+4t7iZqammRdm5KSEqxduxZPP/00Bg0a1OI4udfBclk8Pz8fTz75JFatWiVeFt+5cyfeeOMNvPnmm+LVBDnXgmHIyYwcOfKmp+4jIyNx9OhRAMCIESPEIAQAXl5e+Mtf/oIpU6bg4MGDGDlyJDw8PABADFDXa2hoQOfOndvoCNpOa2tx4sQJbNiwAc888wwiIyNvON4Za9HaOlzv9OnTePbZZ1FWVoakpCRMmzZN7HPGOtwKy0bRlp4nZDluLy+vdltTe2tqasKyZcuQmZmJoKAgrFu3TvxPzdPTU7a1EQQBS5cuRbdu3TBv3rwbjpVzHQCIe8fc3NywZMkSq/2BycnJ2L59O3Jzc1FfXy/7WjAMOZklS5a0apxlz9Cf/vQnm7577rkHAMTrwT4+PgBaPsVZV1eHbt263fJapdaaWphMJkyaNAk9evTAnDlzbjreGWvR2p+Ja/3444/Q6XSoqqrCpEmT8Nprr1n1O2MdboVKpYKrq2uLx2e5PNbSZQFnV19fjzlz5iA3NxfBwcHYunUrAgICxH61Wt3iJUJnr83u3btx5MgRbNq0CV26dLnhWDnXAfhj7YGBgTYvLXd1dUVERATOnDmDs2fPyr4WDEMyZXlwmr0kb2mzfDq2jC0tLbUZe/78eRiNRrt3GjiDc+fO4ccffwQAu6fDa2trERERgejoaOzcuVPWtbD47rvvkJqaiitXrmDWrFmYO3euzRi518Hd3R29evWye3xA83H7+fnZ/AchBxcvXsSMGTNQWFiIe++9F1u2bLEJtpa76a5evWp1uzUAlJWVwdXVFX379m3PZbeZL774AgAwc+ZMu/3PPPMMAODLL7+UdR0AICgoCG5ubi2e8bFcTvby8pJ9LRiGZEqr1QIAvv/+e5szIj/99BMAICIiAgDQq1cv9OrVC0eOHIHZbLa67faHH34AYD9IOAO1Wo3U1FS7fZs2bYKHhwemTp0q3g0h51oAwPHjx/HSSy/h6tWrWLJkic3zZizkXgeg+XckOzsbJSUlVsGuvLwcp0+fxqOPPtpxi5OI0WiETqdDYWEhoqOjsX79ert3B2m1WuTl5aGgoMDqMrvRaMTx48cRHh7e4nO6HN1TTz2F6Ohom/Z///vfKCwsxFNPPYXAwECo1WpZ1wFovhzev39/FBYWQq/Xix+CgOYgVFRUBF9fXwQEBMi+FnwCtYw9/fTTgkajEbKzs8W2uro6ISEhQejXr5/wyy+/iO2rV68WNBqNsH37drHN8rThyMhIobKysl3X3h60Wq3dJ1DLtRZ1dXXCo48+Kmg0GmHr1q03HS/XOlh899134hOoTSaTIAjyfwL1m2++KWg0GiEpKcnqacPXKy4uFu655x4hKSlJMBqNYrtcnjZszxtvvGHzBGol1CEjI0PQaDTC888/b/Vk6Y0bNwoajUZ48803BUGQfy34bjIZO3XqFJKTk1FTU4PY2FgEBgYiNzcXer0eM2bMwIIFC8SxdXV1GD9+PPR6PUaPHo2goCDZvYfqevfffz/UarXNu8nkWoutW7firbfegq+vb4vHEBoaiscffxyAfOtwrblz5+LAgQOIjIzEkCFDcOzYMRQUFGDMmDFYs2aNrF7mXFFRgZiYGDQ2NmL8+PHinYPXmzlzJjw8PPDuu+9i8+bNCAsLQ0xMDE6ePImcnBxERUVh+/btzv0eKjv+9re/YceOHTbvJpN7HQRBQGpqKg4dOoTw8HAMHz4cxcXF4n6yffv2iXuB5FwLhiGZO3v2LN5//33k5uairq4OISEhmDJlCiZMmGAz9sKFC1i9ejW+/vpr1NfXIzQ0FNOnTxf/c5SblsIQIM9avPjii/jyyy9vOGbEiBFYt26d+G851uFajY2N2LRpEz7++GOUl5ejV69eGDduHGbMmOHUf9jtOXToEF566aWbjsvPz4darYYgCNizZw/27NkDg8EAf39/jBo1CqmpqU69UbYlLYUhJdShqakJu3btQkZGBgwGA3x9fTFixAjMmTMHfn5+4jg514JhiIiIiBSNT6AmIiIiRWMYIiIiIkVjGCIiIiJFYxgiIiIiRWMYIiIiIkVjGCIiIiJFYxgiIiIiReO7yYjIKeTl5Ykv0WyNL7/8Er1795ZwRUQkFwxDRORUOnfujBEjRrRqHBFRazAMEZFT8fPzw7vvvtvRyyAiGeGeISIiIlI0hiEikrWIiAgkJCTg8OHDGD16NO677z6MHTsWFy5cwOLFixEREYH8/HzMmjULkZGRePDBB5GZmSl+/YkTJ7Bw4UI89NBD6N+/Px5++GG88sorKC4uvqXvRUSOi2GIiGTv/PnzePHFF+Hl5YVhw4ZBrVaje/fuYv+rr76KY8eOYfjw4fDy8sI999wDoPlN7wkJCdi/fz+6deuGESNGwM/PD9nZ2Rg/fjxyc3Nv+XsRkePhniEikr2KigqMHj0aaWlpcHFxgdlstuqvrKzEJ598gp49e8JsNsPV1RXnz5/HggUL0NTUhHfeeQdPPvmkOH7fvn149dVXMX/+fHz++edWYedm34uIHA/DEBE5lbKyMkRERNxwzDPPPIOlS5datU2ZMgUuLi4AAFdX65Pio0aNQs+ePa369u7di/r6eiQlJVkFIQCYMGECjhw5gszMTKSnp+Oll15q9fciIsfDMERETqU1t9b379/fpq1fv34tjrcXrvLz8wEAY8eOtfs1cXFxyMzMFMe19nsRkeNhGCIip3I7t9a7urpCrVa32O/r62vTdv78eQBAYGCg3a+xPNDx+s3RN/teROR4eP6WiGTPcsnqVvoFQbjh11r2Arm7CxErngAAAUVJREFUu9/S9yIix8MwRERkR48ePQAAZ86csdtvae/WrVu7rYmIpMEwRERkx+DBgwEAn3/+ud3+gwcPAgCio6PbbU1EJA2GISIiOyZOnIjOnTsjIyMD+/fvt+r76KOPkJ2dDW9vb4wbN66DVkhEbYUbqInIqVRXV2PBggU3HTd69GiMHj36tr9PQEAA3n77bcybNw8LFy7Ehx9+iL59+0Kv16OoqAidO3fG3//+dwQEBNz29yAix8AwRERO5cqVK/jkk09uOq5v3753FIaA5kC1b98+bN68Gd9//z1OnjyJHj16IDExEdOnT0dISMgdzU9EjsFFsNwyQURERKRA3DNEREREisYwRERERIrGMERERESKxjBEREREisYwRERERIrGMERERESKxjBEREREisYwRERERIrGMERERESKxjBEREREivb/jI4hiLe6gAgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize = (6, 6)\n",
    "\n",
    "# Calculate the residuals \n",
    "residuals = final_pred - y_test\n",
    "\n",
    "# Plot the residuals in a histogram\n",
    "plt.hist(residuals, color = 'red', bins = 20,\n",
    "         edgecolor = 'black')\n",
    "plt.xlabel('Error'); plt.ylabel('Count')\n",
    "plt.title('Distribution of Residuals');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The residuals are close to normally disributed, with a few noticeable outliers on the low end. These indicate errors where the model estimate was far below that of the true value. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}