Task1 ( Supervised Learning).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Sparks Foundation - Machine Learning/ Data Science Tasks\n",
    "***\n",
    "- Created by Aparajita Das\n",
    "***\n",
    "#### Q1. (To Explore Supervised Machine Learning)\n",
    "\n",
    "In this regression task we will predict the percentage of marks that a student is expected to\n",
    "score based upon the number of hours they studied. This is a simple linear regression task as it\n",
    "involves just two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1 : Importing Libraries and Loading Dataset\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aparajita Das\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:144: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import yellowbrick as yb\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = pd.read_csv(\"t1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Task 2 : Exploratory Data Analysis\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      "Hours     25 non-null float64\n",
      "Scores    25 non-null int64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "t1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = t1.iloc[:, :-1].values\n",
    "y= t1.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.1, random_state= 0)\n",
    "\n",
    "#feature scaling\n",
    "#from sklearn.preprocessing import StandardScaler\n",
    "#sc_X = StandardScaler()\n",
    "#X_train = sc_X.fit_transform(X_train)\n",
    "#X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Scores')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiHiYglxERMTDFOQiIiIepiAXERHxMAW5iIiIhynIRUREPExBLiIi4mEKchEREQ+Lq2gHx3GYPn06eXl5BAIBRo8eTSgUim6fP38+8+fPx+/3k52dTa9evaLbZs+eTWFhIaNGjQJg1qxZLFy4kEaNGgFw++2307x5c6ZMmcKBAwdITEzkzjvvjG4XERGRk6swyJctW0ZpaSmTJk0iNzeXGTNmMG7cOAAKCwuZM2cOkydPprS0lAkTJpCZmYm1lmnTprF582b69esXbWvr1q3cddddtG/fPvrcrFmzSEtL4/rrr+fTTz/lrbfe4mc/+1k1dFVERKTuqXBqPScnh6ysLADS09PZsmVLdNvmzZvJyMggEAgQDAYJhULk5eVRUlLCwIEDyc7OPqatbdu2MXPmTCZMmMDMmTN/1H6PHj1Yu3ZtzDonIiJS11U4Ig+HwwSDwehjn89HeXk5fr+foqKiY7YlJiZSVFREcnIy3bt3Z/Hixce0dd5553HppZcSDAZ5+umnWbFixTHtN2jQgKKiokoVnpqaWqn9Tkd1tl2beaHf4eLDlDdOiVl7KTFsqyb4U5qQGIPj5IVjXR3qY7/rY5+hfvW7wiBPTEwkHA5HH1tr8fv9AASDQYqLi6PbwuEwSUlJx23HWssVV1wRDe2ePXuybds2EhMTo20UFxef8PX/adeuXZXa71SlpqZWW9u1mWf6XViAs78wJk2lNE6hMEZt1RRfYQEFVTxOnjnWMVYf+10f+wx1s98n+8Wkwqn1jIwMVq1aBUBubi5paWnRbR07dmTDhg2UlJRQVFREfn4+bdu2PW474XCYe+65h+LiYqy1rFu3jvbt25ORkcHKlSsBWLVqFZ06dTqlzomIiNRnFY7I+/bty5o1axg/fjzWWsaMGcOsWbMIhUL07t2bYcOGMXHiRBzHYeTIkcTHxx+3nWAwyA033MDDDz9MXFwc3bp1o2fPnnTp0oXnn3+eCRMmEBcXx69+9auYd1JERKSuMtZa63YRp0NT67HlmX7v2YmTuy4mTXlyaj29K4TaVKkNzxzrGKuP/a6PfYa62e8qTa2LiIhI7aUgFxER8TAFuYiIiIcpyEVERDxMQS4iIuJhCnIREREPU5CLiIh4mIJcRETEwxTkIiIiHqYgFxER8TAFuYiIiIcpyEVERDxMQS4iIuJhCnIREREPU5CLiIh4mIJcRETEwxTkIiIiHqYgFxER8TAFuYiIiIcpyEVERDxMQS4iIuJhCnIREREPU5CLiIh4mIJcRETEwxTkIiIiHqYgFxER8TAFuYiIiIcpyEVERDxMQS4iIuJhCnIREREPU5CLiIh4mIJcRETEwxTkIiIiHhZX0Q6O4zB9+nTy8vIIBAKMHj2aUCgU3T5//nzmz5+P3+8nOzubXr16RbfNnj2bwsJCRo0aBcAnn3zC+++/j8/nIy0tjdtuuw2fz8e4ceMIBoMAtGzZkjFjxsS6nyIiInVShUG+bNkySktLmTRpErm5ucyYMYNx48YBUFhYyJw5c5g8eTKlpaVMmDCBzMxMrLVMmzaNzZs3069fPwBKSkp47bXXeOaZZ0hISOC5555j5cqVZGZmAvC73/2u+nopIiJSR1UY5Dk5OWRlZQGQnp7Oli1bots2b95MRkYGgUCAQCBAKBQiLy+PUCjEwIEDyczMJD8/P/JGcXE8+uijJCQkAJGRfiAQIC8vjyNHjvDYY49RXl7ODTfcQHp6enX0VUREpM6pMMjD4XB02hvA5/NRXl6O3++nqKjomG2JiYkUFRWRnJxM9+7dWbx48TGvS0lJAWDOnDkUFxeTmZnJjh07GD58OBdddBG7d+/miSee4LnnnsPv95+0rtTU1FPta6VVZ9u1mRf6HS4+THnjlJi1lxKDtqy1OPsLKNu9E+fwQXAccMohLoC/aQv8zVrga5SCMabK7+VPaUJiDI6TF451daiP/a6PfYb61e8KgzwxMZFwOBx9bK2NhmwwGKS4uDi6LRwOk5SUdMK2HMfhL3/5C7t37+aee+7BGEOrVq0IhUIYY0hNTSU5OZmCggKaN29+0rp27dpVYedOR2pqarW1XZt5pt+FBTj7C2PSVErjFAqr0Jb91zewYQ3s2QnF4ZPvHJ8AbdvBWWdDqDXGd3rnmfoKCyio4nHyzLGOsfrY7/rYZ6ib/T7ZLyYVBnlGRgYrVqzgvPPOIzc3l7S0tOi2jh078re//Y2SkhLKysrIz8+nbdu2J2zrhRdeIBAIcN999+H7/oNs0aJFfP3119x2223s27ePcDhMkyZNTqV/IjXGWgu7dsBXK+Gb7z8oEpPgzI7QshU0agzGBz4flJZAwXdQuA/27oYtOZE/DRKxnbpBRiYmPt7dDomI51UY5H379mXNmjWMHz8eay1jxoxh1qxZhEIhevfuzbBhw5g4cSKO4zBy5EjiT/DBtHXrVhYtWkSnTp145JFHALj88ssZMmQIzz//PBMmTMAYwx133FHhtLqIG+yRYvj8Q8j7/jyR1LbQpSeckXriafM2Z0Veay18uwe25cL2zbD6C1j/JfacLOjUDRNQoIvI6THWWut2EadDU+ux5Zl+79mJk7suJk2dytS63b0TliyAosORkXefCzBNT/71zwnbKimBjWtg/ZdQcgSCSZH20tpX+FpfelcItTmt9z3KM8f6eA4diPw5DU1SmlBQWBDjgmpYcqPIn0ry9LGugrrY7ypNrYvUd/arVbDys8iUeVY/6NLjtL/jBiLT6d16YzMyYf0q+GoVfPhPbJuzoO8FmKSGsSu+rjl04LR/kStvnBKz8yvc4kvvekpBLvWDglzkBKy1kSnwdSsgmAwDL8U0PyNm7Zv4eMjqh22XHpmy37kdvtmFPXcQ5qyOMXsfEanbtESryHFYa2H5p5EQb9gYLr0mpiH+Q6ZxE7j4ajh3UOTStY/nYpcsxJaWVsv7iUjdohG5yPEs/wRy1kLjpjB0OCZ44ssqY8EYA2efg23ZCj6ZFzm7/ds92EGXYRo3rdb3FhFv04hc5D/YjesiIZ7SFC65utpD/IdM4yZw2X9B5+5woBDefwv79dYae38R8R4FucgP2N07YdnHkJAIg6/ANEis8RqM34/pfT4MuBisjZwIt/pzPHqBiYhUM02ti3zPHiiEjz4AY2DQZZhkd88eN+3OxqY0gcX/hLUroHAftl06VV/oVUTqEo3IRQBbVgaL50Su6z53EKZlK7dLAsA0aQ6XXwuh1rBjG870Z7H7/uV2WSJSiyjIRQBWLYX9BZDRDdOhk9vVHMMkNICLroSzz4HdO3Aevxe7fZPbZYlILaEgl3rP7t4JOWugUQr07O92OcdlfH7oNxAz7L/gQAHO0w9gV3zqdlkiUgsoyKVesyVHIkuvGgMDhmLiau9pI8YYfOddhO/Oh8D4caY9iTP7dZ0EJ1LPKcilflv2cWT99G69Mc1aul1NpZjuffHdPxmatsC+8xfsS89p8RiRekxBLvVW6dfbYGsuNGsJ3Xq6Xc4pMW3a4XvwGWiXjl26COfZ8diD+90uS0RcoCCXesmWl1P08dzIlPq5gyLfQXuMadwE372TMH0ugM0bIifB5X/tdlkiUsMU5FI/5azBKfgOzu5y2rcjrQ1MfALm5/diht8A//oG58lx2HUr3C5LRGqQglzqHVt0GNYsj6zaltXX7XKqzBiD76obMD+/F0pLcaY8irNwlttliUgNUZBL/bNyCZSVkth/UOQa7TrC1/dCfPdOgoaNsH97Aeev07Dl5W6XJSLVTEEu9Yr9dg9s2wTNWhDfubvb5cSc6dAJ34P/H7Q5C7v4fZwpj2CLDrldlohUIwW51BvW2sgKbgC9B2B8dfN/f9OsBb7fTIbMPrB+Fc4T47B7d7tdlohUk7r5SSZyPLt3wje7oPWZtWYt9epiGgTx3fkg5uKrYc9OnCfu1UlwInWUglzqBWstrP5+NJ7Vz91iaojx+fFdfyvmpjuhOIzz/x7GeftlbHmZ26WJSAwpyKV+2LENvvsWzuzg6cvNTofvwkvx3f80tAhh57zF3vt/gf1ur9tliUiMKMilzrOOA6s/jyz+0t37l5udDnNmB3zj/xfTewAl67/E+d0vcT6eq3XaReoABbnUfds3RW5R2j4D07iJ29W4xgSTMLffR9O7J4Ix2Bm/x5n6qO5vLuJxCnKp06y1sG4lGB9k9na7HNcZY0i6eDi+302Fc7Jg7XKc347B+eBtbJluvCLiRQpyqdt2bo+MxtudjUlu5HY1tYZp2gLf3Q9jbr4LAgHsm3/GefhX2HUrNd0u4jEKcqmzoqNxgC493C2mFjLG4LvgEnyPTcMMGgbf5OP8v9/hPP0ANned2+WJSCXFuV2ASLX5Zhf86xto2w6T0tTtamotk9QQM+oO7AWX4vzjVfjyC5ynH4SMbviGDofMPp68O5xIfaEgl7orOhr31r3G3WLS2uO/azx260acd/8K61fjbFwLzVpiBg7D9L0Q06yFa/XZsjL49hvsju1w+GDkT9EhOFIMJUcifxznaG8gLg4aJEKDICQlc6R1GjYxCA1T6uyqflI/KcilTrLf7YXdO+CM1pgWZ7hdjqeY9hn4/+cR7M7t2EWzsUsXY99+Gfv2y5Ez/3udjzknC1LTqiUQ7eGDsCcf+00+7NmJ3R35m293w4luAuOPg/h4iM4cWAgXRc6P+F7R+tWR/4gLYFPbQtv20OZMTHxCzPsgUpMU5FI3ffX9h3ZXjcZPl2lzFuamO7HZP8Wu+AS7/FPIWYvduhELEEyGs8/BpLWHVm0xoTbQrAUkJmGMOWG7tqw0ErAF30Hhd9hvdsE3u7B7d8E3+XDo4I9fFEyGs87GNG4SOfchqSEkN4w836ABxn/8jzLrlENxGA4eIDF8mPCuHbB3N3y9NfLH58OmdYBzslydbRCpCgW51Dn28EH4egs0aQat2rhdjueZpGTMhZfBhZdhDxRi1y6HjesiJ8R9+QX2yy8AiJ7r7vdHgrZBYuSyP2MiU95HwpFRcsmR47+R3w/NQ9C+EybUGs5IxYTaQqg1NGwc+eVgz06cUzgRz/j8kbAPJtOgcQrFZ50d+UVgf0EkyLdviv6xZ6RGzgcIta7aP5hIDVOQS92zcR1YC50yTzoylFNnGqVgzh8K5w8FiCwms3sHdveOyDR44T44dAAOHoh8d42NhLgxke+qU5pBYjCyME9KU0hphmnRCs5IheZnYOKq/yPJGPP9ezfFdusV+Qpm/erITXXmvYs9swP0Oh+TlFzttYjEQoU/NY7jMH36dPLy8ggEAowePZpQKBTdPn/+fObPn4/f7yc7O5tevXpFt82ePZvCwkJGjRoFwPLly3nrrbfw+XwMHjyYoUOHUlJSwpQpUzhw4ACJiYnceeedNGqk633l9NiyUti0HhIaQLuz3S6nzjNNm0PT5hiPXt5njIHUNEhNi5xX8cXHkLcF8vOw3ftC5+76ZVBqvQrPVFm2bBmlpaVMmjSJG2+8kRkzZkS3FRYWMmfOHB599FEeeughXn31VUpLS6Ph/MEHH0T3LSsr4+WXX+ahhx7i4YcfZsGCBRQWFjJ37lzS0tJ45JFHuPDCC3nrrbeqp6dSP2zNjUzdpnc54femIsdjmrWEy7Kh/+DIyXMrlsCi2dgjxW6XJnJSFQZ5Tk4OWVlZAKSnp7Nly5bots2bN5ORkUEgECAYDBIKhcjLy6OkpISBAweSnZ0d3Tc/P59QKERycjJxcXFkZGSwYcOGY9rv0aMHa9eujXUfpZ6w1kLO2sj3suld3S5HPMgYg+nYGYaPhFZtIf9rmPUadu9ut0sTOaEKhyzhcJhgMBh97PP5KC8vx+/3U1RUdMy2xMREioqKSE5Opnv37ixevPiE7Rzd94fPN2jQgKKiokoVnpqaWqn9Tkd1tl2beaHf4eLDlDdOOe620h3bOLR/H/HpXUhqVbkTllJO0FZt5U9pQmIMjpMXjvXxnOz4V0alj3fjFGz2TyhesYTizz+Cee8SvPgq4s8+57TfOxZO5/h79VhXVX3qd4VBnpiYSDgcjj621uL3R67VDAaDFBf/e9opHA6TlJR0wnaOt+8Pny8uLj7h6//Trl27KrXfqUpNTa22tmszz/S7sABnf+FxN9nlS6VrWH8AACAASURBVAAo6dCJ0hPs80MpjVMorMR+tYmvsICCKh4nzxzr4znJ8a/IaR3vs7tAcmP48J8c/uAdDhfsw2S4N9tzqsff08e6Cupiv0/2i0mFU+sZGRmsWrUKgNzcXNLS0qLbOnbsyIYNGygpKaGoqIj8/Hzatm173HZat27N7t27OXToEGVlZWzYsIH09HQyMjJYuTKyAteqVavo1KnTKXVOBMAeOgD5eZEzn5trARiJHdOqDVxydeRyui8+wn75hW4sI7VKhSPyvn37smbNGsaPH4+1ljFjxjBr1ixCoRC9e/dm2LBhTJw4EcdxGDlyJPHx8cd/o7g4br75ZiZNmoTjOAwePJimTZtyySWX8PzzzzNhwgTi4uL41a9+FfNOSj2waX3k7/Qu7tYhdZJp2gJ7aTYseA/WLI882b2vu0WJfK/CIPf5fNx+++3HPNe69b+/fxw6dChDhw497msHDRp0zOPevXvTu/ex94ROSEjg17/+dWXrFfkRW14OmzdAfAKc2dHtcqSOMo0aYy+9Bj6YCWuWYxMaYDplul2WiG5jKnXAzm2RZTjbZ9TIgiJSf5lgEgwdDolBWPYJdmuu2yWJKMilDsjVtLrUHNOwMVx0JQTiYclC7K6v3S5J6jkFuXiaPVAYuTPWGamRZT9FaoBp0hyGXBFZevbjedgD+90uSeoxBbl426avIn+frdG41CzTshWcOzCykuDiOdjSUrdLknpKQS6eZcvLYHMOJCRCWnu3y5F6yHToBBndYP++yDS7LksTFyjIxbu+3hYZDXXIwHy/SJFIjet9HrRsFbl17vrVblcj9ZCCXLxry4bI3x07u1uH1GvG54cLL4XEJFj1eeQuaiI1SEEunmQPHYzcP7pFSCe5ietMYhDOHwLWgU/m6/tyqVEKcvGmLTmRvzUal1rCtGoLnbvDgUJY8anb5Ug9oiAXz7HWRoI8LqCV3KR26XEuNGkGm9Zjd2xzuxqpJxTk4j27d8Lhg3BWR0wg4HY1IlHG74cBF4PfD58txh4prvhFIlWkIBfv0UluUouZlKaRG6ocCcOKJW6XI/WAglw8xRYdjlx21rgJ6HalUlt17g5Nm8OWHOzuHW5XI3Wcglw8xa5bAU45dOiEMcbtckSOy/h8cO7gyBKuSz/Eluksdqk+CnLxFPvlF5H/aJfubiEiFTDNWkRG5ocO/Pse5iLVQPd8FM+w3+6Br7dCqE3kdpL1UcmRyE1iqiBcfBgKC2JUUA0r9tjJY937RP6fXb8a2y4d06SZ2xVJHaQgF8+wny+O/Ef7ejwaLyrC2b6pSk2UN07B2V8Yo4Jqlq9la7dLOCUmLoDtewEsnA3LP8EOvUpfCUnMaWpdPMFai136IQQCukGKeIppfSa0ToM9+bBjq9vlSB2kIBdv2L4JvsnHdOqOCcS7XY3Iqek9AHw+WL4EW1bmdjVSxyjIxRPs0sUAmKy+7hYichpMoxTolBlZyEh3SJMYU5BLrWfLyrDLPoaGjaGDFoERj+rWGxKDsG4l9vBBt6uROkRBLrXf+lVwcD+m74W677h4lomPj6zFXl4GXy5zuxypQxTkUutFp9X7DXK1DpEqa5cOKU0jK74VfOd2NVJHKMilVrPhIuzqz+GM1nCW7nQm3mZ8PujZP/Jg1WfuFiN1hoJcajW78jMoLcGcO0jX30rdkJoGZ6RC/tfYPfluVyN1gIJcarWji8CYfgPdLUQkRowx/x6Vr/wMa627BYnnKcil1rIF30HOGujYGdMi5HY5IjFjmp8BZ3aA7/ZGlnAVqQIFudRa9ouPwFrMuYPdLkUk9rLOjdwd7csvsI7jdjXiYQpyqbXs0kXgj8P0Pt/tUkRizjRqDO0zYH8BbN/sdjniYQpyqZXszm2wczt0641Jauh2OSLVI7N3ZOnWNcs0KpfTpiCXWsku/RAA37mD3C1EpBqZ5EbQsTMc3A/bct0uRzxKQS61jnXKsZ9/CMGkyIhFpC7r2vP7UflyrFPudjXiQQpyqX02roPC7zC9ztedzqTOM0kN4ewucOgAbM5xuxzxoLiKdnAch+nTp5OXl0cgEGD06NGEQv++FGj+/PnMnz8fv99PdnY2vXr14sCBA0yZMoWSkhKaNGnCmDFj2L17N3/+85+jr9u0aRP33XcfHTt25Fe/+hVt27YFoG/fvlx++eWx76l4RvTacU2rS33RtSdsXh+5oUrHzpEV4EQqqcIgX7ZsGaWlpUyaNInc3FxmzJjBuHHjACgsLGTOnDlMnjyZ0tJSJkyYQGZmJm+++SYDBgxg0KBBvPPOO8ybN48rr7yS3/3udwB89tlnNGnShKysLNasWcP555/PLbfcUq0dFW+wJUewK5ZAs5bQ8Ry3yxGpESaYhO3YOTIbtX1T5Gx2kUqq8Ne+nJwcsrKyAEhPT2fLli3RbZs3byYjI4NAIEAwGCQUCpGXl8fGjRujr8nKymLt2rXR1xQXF/P666/zs5/9DICtW7eybds2Jk6cyLPPPktBQUFMOyjeYr/8AorDmH4DNSqR+uWcHmB8kVG5VnuTU1DhiDwcDhMMBqOPfT4f5eXl+P1+ioqKjtmWmJhIUVHRMc8ffe6ohQsX0r9/fxo1agRA69atad++PZmZmXz88ce89NJL3HPPPRUWnpqaWvlenqLqbLs2qw39/nb1UoqBM4ZfR+A49YSLD1PeOCVm75cSw7ZqgpMUxBeDmr3W76Oq2v9a3e/GKRzO6EJJzlqSvttLfIcfj8r9KU1IPMWf09rwc+2G+tTvCoM8MTGRcDgcfWytxf/9PaGDwSDFxcXRbeFwmKSkJILBIOFwmPj4+OhzR33yySf8+te/jj7u2rUrCQkJQOT78ddff71She/atatS+52q1NTUamu7NqsN/bYH9+OsWAJpHfg2rgEcr57CApz9hTF5v5TGKRTGqK2a4ktIqnL/vdjvo6rSfy/026Z3hZy1HP7iIw43a/mjGwX5CgsoOIWf09rwc+2Gutjvk/1iUuHcZUZGBqtWrQIgNzeXtLS06LaOHTuyYcMGSkpKKCoqIj8/n7Zt2x7zmtWrV9OpUycAioqKKC0tpXnz5tE2pk2bxtKlSwFYu3Yt7dq1O40uSl1gl30M5eWY/oPcLkXEFaZxE0jrAN99C7t3uF2OeESFI/K+ffuyZs0axo8fj7WWMWPGMGvWLEKhEL1792bYsGFMnDgRx3EYOXIk8fHxZGdn8/zzz7NgwQIaNmzI2LFjgcgoukWLFse0P2rUKP7whz8wd+5cEhISGD16dPX0VGo9u3QxGB+mz4VulyLinm494estsG5l5JanIhUw1qNnVWhqPbbc7rf9ZhfO+NHQpQf+ux8+8Y57duLkrovJe3phqvU/+Vq2xtlbtXtYe7HfR1Wl/17qt10wC3Z9DZdeg2nZKvq8L70rhNpUuh23f67dUhf7XaWpdZGaYJcuBnTtuAgA3XpF/l63wt06xBMU5OI6a21kEZiEBpge/d0uR8R1pmUraNkK8r/G7vuX2+VILacgF/dt3Qjf7sH0OBeT0MDtakRqh64alUvlKMjFddFp9X6DXK1DpFZJbQtNW0DeFuwBb3y3L+5QkIurbFlp5LKzxk2gc3e3yxGpNYwxkTXYIXIGu8gJKMjFXetWwuGDmD4XYr5faEhEvpfWHhqlwNZc7OFDblcjtZSCXFyls9VFTswYA116gHUgZ43b5UgtpSAX19iiQ5GbpLRqGxl5iMiPtUuHBomwaT22OFzx/lLvKMjFNXbFEigrxZw76EdrSotIhPH7oVMmlJZEfmZE/oOCXFxjP/8QANNvoMuViNRy6V3AH4f9bCG2rMztaqSWUZCLK+x338LGtZDeBdOspdvliNRqJqEBdOwM+wuwKz51uxypZRTk4gr7xfej8XMHu1yJiEd0zgRjsHPfwaO3yJBqoiCXGmetxX62COLiML3Oc7scEU8wDRtjzukRuTPaxrVulyO1iIJcat6ObZF7LWf2xQST3a5GxDPM+RcB4Mx9x+VKpDZRkEuNs0sXAeDTteMip8S0bQcdz4G1y7H5X7tdjtQSCnKpUdYpx37xESQ1/PetGkWk0nyXjgDAztOoXCIU5FKzNqyB/QWYXudj4gJuVyPiPZl9oWUq9vPF2MJ9blcjtYCCXGqUXbIQAHPeEJcrEfEm4/NhLhkBZWXYRbPdLkdqAQW51BgbLsKu/gxapkL7DLfLEfEs038wJDfCLp6jZVtFQS41xy7/BEpKMP0Ha0lWkSow8QmYwVdA0SHspwvcLkdcpiCXGmM/+35avb8WgRGpKjP4cgjEY+e/i3XK3S5HXKQglxphv90Dm9ZDRjctySoSA6Zh48i5Jv/6BlYtdbsccZGCXGqE/Sxy7bhOchOJHTP0ajAG54OZWra1HlOQS7Wz1kYWgYlPwPTUkqwisWJCraF7X9iWC5s3uF2OuERBLtVv03r4dg+m53mYBoluVyNSp/guuQYAZ+5MlysRtyjIpdpFT3LTtLpI7HXsDO3S4csvsHt2ul2NuEBBLtXKHjkSueysaXPI6OZ2OSJ1jjEG36XXgLXYee+6XY64QEEu1cquXgrFYcy5gzE+/e8mUi16nAstQtglC7EHCt2uRmqYPlmlWkWXZNW14yLVxvj8mKFXQVkpdtH7bpcjNUxBLtXGFnwHG76EdumYUBu3yxGp08z5QyGpIXbxbOyRI26XIzVIQS7Vxn6+GKyjk9xEaoBJaBBZ7e3QQeyS+W6XIzVIQS7VwlobmVaPi8P0ucDtckTqBTP4cogLYOdp2db6JK6iHRzHYfr06eTl5REIBBg9ejShUCi6ff78+cyfPx+/3092dja9evXiwIEDTJkyhZKSEpo0acKYMWNISEjgpZdeYuPGjSQmRq4lHjduHGVlZcfdVzwubzPs3gG9zsMkNXS7GpF6wTRqgjlvCPajDyLLtrZp63ZJUgMqDPJly5ZRWlrKpEmTyM3NZcaMGYwbNw6AwsJC5syZw+TJkyktLWXChAlkZmby5ptvMmDAAAYNGsQ777zDvHnzuPLKK9m2bRsPPfQQjRo1irb/0ksvHXdf8bajJ7n5+l/kciUi9Yu5+Grsx3Mjy7Zeea3b5UgNqHBqPScnh6ysLADS09PZsmVLdNvmzZvJyMggEAgQDAYJhULk5eWxcePG6GuysrJYu3YtjuOwZ88eXnjhBSZMmMDChZEP+uPtK95mS45Evh9v3AS69HC7HJF6xYTaRJdtLflqtdvlSA2ocEQeDocJBoPRxz6fj/Lycvx+P0VFRcdsS0xMpKio6Jjnjz535MgRLrvsMq688kocx+Hhhx+mQ4cOx923MlJTU0+po6eiOtuuzWLV78OL5rCv6DANr7uOlLS0mLR5VLj4MOWNU2LWXkoM26oJTlIQXwxq9lq/j6pq/73a76P8KU1IrMTP6ZFRP2fv6s858NYMUif+bw1UVvvUp8/xCoM8MTGRcDgcfWytxe/3AxAMBikuLo5uC4fDJCUlEQwGCYfDxMfHR59LSEjg8ssvj37/3bVrV/Ly8o67b2Xs2rXrlDpaWampqdXWdm0Wy36Xv/c6AIe7n0tRrP8tCwtw9sdmwYuUxikUxqitmuJLSKpy/73Y76Oq0n8v9/soX2EBBZX5mUppCR06UfzFx+Sv+ALTqn5d/lkXP8dP9otJhVPrGRkZrFq1CoDc3FzSfjDC6tixIxs2bKCkpISioiLy8/Np27btMa9ZvXo1nTp1YteuXfz2t7/FcRzKysrIycmhXbt2x91XvMvu3QUb10buO35G/fmNWKS28V0yAgA77x2XK5HqVuGIvG/fvqxZs4bx48djrWXMmDHMmjWLUChE7969GTZsGBMnTsRxHEaOHEl8fDzZ2dk8//zzLFiwgIYNGzJ27FgaNGjAgAEDeOihh/D7/Vx44YW0bdv2uPuKd9lPItevmgEXu1yJSD2X1Y+41LaUfbYQe/UoTOMmblck1cRYj96NXlPrsRWLftvycpzf3AKlJfie/jMmvhouI9yzEyd3XUya8uJUq69la5y9+VVqw4v9Pqoq/fdyv4/ypXeFU1glsdGqJRT832TM5dfju+Yn1VhZ7VIXP8erNLUuUmlrl8P+Aky/QdUT4iJySoJDr4TkRtjF72OLK3cisXiPglxixvl4LgDmgktcrkREAHwJDTAXXQlFh7AffuB2OVJNFOQSE/a7b2HtCjjrbEzbdm6XIyLfM4OvhAaJ2HnvYEtL3C5HqoGCXGLCfvRB5AYpg4a5XYqI/IBJSsYMuhz2F2A/1c1U6iIFuVSZLSvDfjIXgkmY3rpBikhtYy6+CgLx2H++jS0rc7sciTEFuVTd6qVwoBDTfwhGN7wRqXVMoyaRS0K/24v94iO3y5EYU5BLlTmL5wBgBmpaXaS2Mpdmg9+PnfMm1nHcLkdiSEEuVWJ37/z3Sm71bBlIES8xzVpgzh0Me3bCqs/cLkdiSEEuVWI/jIzGfTrJTaTWM5f9Fxgfzvtv4NG1wOQ4FORy2uyRYuxnC6FRCmT1c7scEamACbXG9D4fvt4K61a6XY7EiIJcTpv9bBEUHcZceCkmLuB2OSJSCebyawFw3n/D5UokVhTkclqstdiFs8Afp5PcRDzEtGkHmX1g83psjO5bIO5SkMvpWb8adu/A9D4fk9LU7WpE5BT4Lr8OAGe2RuV1gYJcTouz4D0AzEVXuVyJiJwq06ETdMqE9auw2za5XY5UkYJcTpn9ZlfkTmcdOmHane12OSJyGqKj8ll/d7kSqSoFuZwyu3AWAOai4S5XIiKnrVMmpHeFNcuwWze6XY1UgYJcToktOoz9dAGkNMP06O92OSJymowx+K66EQDnH6+6XI1UhYJcTon9+AM4EsYMuQITF+d2OSJSBSajK3TuDl+twm5e73Y5cpoU5FJptrQUO+8fkJCIGXiZ2+WISAz4rroBAOddjcq9SkEulWY/Xwz792EGXooJJrtdjojEgOl4DnTpATlrsBvXul2OnAYFuVSKdRzsB29HFoDRJWcidYrv6lEAODNf0RrsHqQgl8r58gvYk4/pNxDTtLnb1YhIDJl26ZB1LmzJifysi6coyKVC1lqcf74FgLn0GperEZHq4Mu+KXJntLdnYJ1yt8uRU6Agl4ptWg9bN0L3vpjUNLerEZFqYFq1xZw3BHbviNwQSTxDQS4VOrryk++y/3K5EhGpTuaqGyAugP3Hq9jSErfLkUpSkMtJ2c3rYcOX0Lk7pmNnt8sRkWpkmrbADLkS9v0Lu3C22+VIJSnI5aSc974fjQ+/weVKRKQmmMuvhcQk7PuvYw8dcLscqQQFuZyQ3bwhcrvSzt0xZ5/jdjkiUgNMUkPMlf8NRYex//ib2+VIJSjI5YQ0Ghepn8yQK6BlK+yHc7C7d7hdjlRAQS7HZbfkwPpVGo2L1EMmLoDvup+B4+C8/pLb5UgFFOTyI9ZanHf+AoDvypEuVyMirujeL3Kr03UrsOtWuF2NnISCXH7sq5WQswa69sKkd3G7GhFxgTEG33/fGlkk5vWXsGVlbpckJ6Agl2NYpxznrZfBGHz/dbPb5YiIi0ybdpgLLoksErPgH26XIydQ4Q2lHcdh+vTp5OXlEQgEGD16NKFQKLp9/vz5zJ8/H7/fT3Z2Nr169eLAgQNMmTKFkpISmjRpwpgxY0hISGDWrFksWbIEgB49enDddddhrWX06NG0atUKgPT0dG688cZq6q5UxC79EHZux/QfjGnTzu1yRMRlJvsm7Mol2H/8DdvnAkzTFm6XJP+hwiBftmwZpaWlTJo0idzcXGbMmMG4ceMAKCwsZM6cOUyePJnS0lImTJhAZmYmb775JgMGDGDQoEG88847zJs3jz59+vDJJ5/w+OOPAzBx4kT69u1LQkIC7dq14/7776/enkqFbGkJ9t2/QlwAc/VP3C5HRGoBk9QQc+3PsH/+fzh//xP+MQ+6XZL8hwqn1nNycsjKygIio+UtW7ZEt23evJmMjAwCgQDBYJBQKEReXh4bN26MviYrK4u1a9fSrFkzHnzwQXw+Hz6fj7KyMgKBAFu3bqWgoICHH36YJ554gl27dlVTV6UiduFs2PctZsiVmGb6rVtEIkz/wdDxHFi1FLtmmdvlyH+ocEQeDocJBoPRxz6fj/Lycvx+P0VFRcdsS0xMpKio6Jjnjz4XFxdHo0aNsNbyyiuv0K5dO1JTUyksLGTEiBH079+fnJwcpk6dyhNPPFFh4ampqafT30qpzrZrq/KC7+D91zFJDWl1y134GzZ2u6TjChcfprxxSszaS4lhWzXBSQrii0HNXuv3UVXtv1f7fZQ/pQmJp/j5FKvPs5L/+S3f/HIU5vWXCA26FF+DBjFpt7rUp8/xCoM8MTGRcDgcfWytxe/3AxAMBikuLo5uC4fDJCUlEQwGCYfDxMfHR58DKCkp4Q9/+AOJiYncdtttAHTo0CHaXqdOndi3bx/WWowxJ62rukbuqamp9XJWoMHrf8IWHcbccDvfHDwMBw+7XdLxFRbg7C+MSVMpjVMojFFbNcWXkFTl/nux30dVpf9e7vdRvsICCk7h8ymmn2fxQczFV1H+wUx2/fEZfNfdEpt2q0Fd/Bw/2S8mFU6tZ2RksGrVKgByc3NJS/v3bSw7duzIhg0bKCkpoaioiPz8fNq2bXvMa1avXk2nTp2w1vL0009z5plncvvtt+PzRd76jTfeYPbsyOL827dvp3nz5hWGuMSW3ZLD4XnvQZuzMAOHuV2OiNRSZviNkRXf5v0jsmiU1AoVjsj79u3LmjVrGD9+PNZaxowZw6xZswiFQvTu3Zthw4YxceJEHMdh5MiRxMfHk52dzfPPP8+CBQto2LAhY8eOZdmyZaxfv57S0lJWr14NwI033siIESOYOnUqK1euxO/3M2bMmGrvtPybdcpx/vYCAL4bfoH5fnZEROQ/mYQEfD/9Jc7TD+L8eQq+3z6HCcS7XVa9Z6y11u0iToem1mPD+egD7CvPExw0jCOj7nC7nIrt2YmTuy4mTXlxqtXXsjXO3vwqteHFfh9Vlf57ud9H+dK7QqhNpfevrs8z59U/YhfNxgy7Fl927Vtvoi5+jldpal3qLnugEDtzBiQkknLLWLfLERGPMNk3Q/MzsB+8jd22ye1y6j0FeT1mX/0jHDqIGTEKvy43E5FKMg0S8d18V+SmKtOfwRaHK36RVBsFeT1lVyzBrvgUOnbGDLnS7XJExGNM5+6YS6+Bvbuxf3/B7XLqNQV5PWQPHcB5dRrEBfD99JcYn/43EJFTZ0b8BNI6YD9dgLPsE7fLqbf0CV4P2ddehAOFmKtvxJzCiTMiIj9k4gL4fn4vxCdgX3ke+91et0uqlxTk9YxdsQS7dBGc2RFz8Qi3yxERjzOh1pgbbofwYZw/PoUtLXW7pHpHQV6P2O/24syYCvEJ+G79H10zLiIxYc4fGlmPfVuuvi93gYK8nrBlZTh/egaKDmNG/hzTqq3bJYlIHWGMwfxkDLRth/3oA5yP57pdUr2iIK8n7Ht/gy05mD4XYAZc7HY5IlLHmPgEfHc8AEkNsa9Ow27LdbukekNBXg/Yr1Zh57wJzc/A/GSM1rIXkWphWoQiJ7+Vl+M8/zj2u2/dLqleUJDXcXZPPs4LT4Hfj+/2+zDBJLdLEpE6zHTpgbnuFti/D2fKw9iiQ26XVOcpyOswW3QI5/nHIt+L33QXpl262yWJSD1ghl6FuWg47Poa5/+ewJbpTPbqpCCvo6xTHjm5bU8+5pJr8J03xO2SRKSeMMZgrr8Fss6FjWuxL0/FOo7bZdVZCvI6yFqL/ft0WLcSuvbC/FftuzuRiNRtxufHd9s90D4Du3Qx9q9/wKM326z1FOR1kP3Hq9hFs6H1mfh+fi/Gp+vFRaTmmYQEfGMnQlp77EcfYP/2R4V5NVCQ1zHOvHexs16DFiF8dz+sk9tExFUmKRnf/zwCrc/ELnof+/pLCvMYU5DXIc7Hc7GvvwgpTfH9zyOYlKZulyQigkluhO/Xj0Krttj570bWZS8vd7usOkNBXkc4C97Dzvg9JDXEd/cjmBYht0sSEYkyjVLw3Tspcre0j+fiTHsSW1ridll1goLc46y1OLP+jv37n6BxE3z3PY5pneZ2WSIiPxIN806ZsHopznMTsYd1nXlVKcg9zDrlke+b3n0VmrXEN+4JTOsz3S5LROSETGIQ39iJmF7nQ+5XOJN+jc3Pc7ssT1OQe5QtOozz+0nY+e9CqA2+cZMxLVPdLktEpEImEMDcfi/m8uvh2z04T9yHXfGp22V5loLcg+yenThP3Atrl0OXHvjufwrTtLnbZYmIVJrx+fFd8xN8o38DgDPtSZzXXtT35qchzu0C5NQ4n3+I/esfIFyEufQaTPbNuk5cRDzL9DofX6gNzh8mR85o37Aa322/xrRp53ZpnqERuUfYw4dwXngaO/3/A8fB3PprfNf+TCEuIp5nWp+Jb8L/YgZeBvl5OJPuwXn/Da3RXkkakddy1lr48nOcV1+Agn9Bh074bvkfTMtWbpcmIhIzJqEB5idjsJl9cF6eip35CnbpYnyjRmMyurldXq2mIK/F7N5dOH+fHvku3O/HXH0jZth1GL9G4SJSN5nMPvge+T/sO69gP/wnzjMPYfpcgBnxEw1gTkBBXgvZA4XYD97GLpwFZWXQuTu+G27HtGrrdmkiItXOJCVjRt2BPW8ozqvTsMs+xq5cgjn/YsyV/41p0sztEmsVBXktYg8UYue+E7nhSckRaNoc33W3QK/zMca4XZ6ISI0y7c7G98DTsHIJzrt/xX70T+yS+Zh+gzAXX611M76nIK8FbN5m7IJZ2GUfRUbgKU0x1/4MM+BiTCDgdnkiIq4xPh/0HoCvR3/s0kXYOW9hP52P/XQ+nJOF78JLIbNvvf6sVJC7xB4owC77BPv5h7AtN/Jky1TMRVdiLrgEE4h3t0ARkVrE+P2Y84di+w+BZlN9ngAACq9JREFUtctx5r0L61fj/P/t3XtMVHcWwPHvnQF5KIjU6ojFVqSIL5AtEtqyTYXGuK1NtraaJq6UrE2DNiFNQ1nTqsGkWE2IaVTSWBskpq1prI9t+oaqcRNbtkakYhUVFJbXilpAYIAZ7tk/RsbiumLTGWdnPJ+EzHDvbybncvndc+c3957fzydgdARG2hMYaX+EuERfh3rXaSK/i+RSK3LyGFL9TzhzEsQEwwJzUrFkLoKZc11nn0oppW7JsFggOQ1rchrS3IgcrUC+P4Qc+sL1tWRkFFcfz0SmzYKEWRhh4b4O2es0kXuJiMDVduTcKTj3M1JbA/9uvtFgaoLre555j2NEjvNdoEop5aeMyVMwlvwVeS4bfq5Cqn5ATlTS89U+YB9YLK5j7fQ5GFMTIC4hII+3msh/JxGBa51wqRVpb4PWRqSxHhrrXcuHhIRCchpGUirG7FQtqaqUUh5iBAVB0jyMpHnI8lWM77hM+5Fy5HQ1XDiL1J1BhhpH3w8xsRi2WJg0+fpjLEZEpC834XcZMZGbpskHH3xAQ0MDwcHB5ObmYrPdmOu6oqKCiooKrFYrixcv5pFHHqGrq4stW7YwMDDAuHHjWLVqFSEhIb+prS+JiOuis347dF+D7k7o7kKudUF3F1zrRK5cgktt0N7manez+yZASjpG/EyMh2dB7FTXP5tSSimvMSxWQmanYImeCH/+C9Lb40rm9bXIhbPQWAc1x5Ga4wA3EvyYSIgeD2OjMaKiYWy068LjseMgYiyEhkNoGISFQWjY/1VVzREzy48//ojD4aCoqIizZ8+ya9cuCgoKAOjo6OCrr75i48aNOBwO1q5dS1JSEp9++ikZGRk8+eSTHDhwgPLycjIyMu647aJFi7y+4UPMv3+MHPsHDAzAQD9NTgfS3wciI794VAhMmAT32zDuv/44MQamxGGMjvB+8EoppW7LCB8Ns1IwZqW4l0lPN7Q1IW1N0PovpLUJ2q7/NNbz66P//8wEo0JcI61Bwa4hfGsQWK2u50HBWP70vGuq1rtgxER+5swZ5s6dC0BCQgJ1dXXudefPn2f69OkEBwcTHByMzWajoaGB2tpaFi9eDMDcuXPZvXs3NpvtjtvezUROVwf0dLt2SsRYgkaPwYEBo0bBqFDXcMuYCBgzFsZEXv890jU8Exml93crpZSfMUaPgWmJGNOGX+EuImDvhc6r0HEV6bwKnb+4Rmb7eqHPjthdj9h7ob8PBp1gDg5/bprIL5e5W9lhxERut9sJD79x1Z/FYmFwcBCr1Upvb++wdWFhYfT29g5bfqtlI7W9EzExHpp7+29ve+Z9AoDH/qbeFBMDf0jz2NuN8dg7+Rfd7nuHX/RrL7iXtnvEe53CwsKw2298BywiWK/X+g4PD6evr8+9zm63M3r0aMLDw92v+fWyO22rlFJKqTszYiKfPn06VVVVAJw9e5YpU6a418XHx3P69GkGBgbo7e2lubmZ2NjYYa85ceIEiYmJv6mtUkoppe6MIXL7q7qGrlpvbGxERFi1ahVVVVXYbDZSU1OpqKjgu+++wzRNnnvuOdLT0+no6KCkpIS+vj4iIiLIy8sjNDT0N7VVSiml1MhGTORKKaWU+v+l9UCVUkopP6aJXCmllPJjWmoM6O3tZcuWLdjtdpxOJy+99BIJCQlUVlby4Ycfct99rknsly5dysyZM30creeMVLUvkDidTt577z3a29txOBw8//zzREdHs2nTJiZNmgTAggULeOyxx3wcqecVFBS4b/GcMGECTz31FGVlZVitVpKSkliyZImPI/S8w4cPc/jwYQAcDgcXL14kLy8vYPvzuXPn+OijjygsLKStrY2SkhIMwyA2NpYVK1ZgsVjYs2cPx48fx2q1kpOTQ3x8vK/D/t1+vd0XL16ktLQUi8VCcHAwr776KlFRUZSWllJbW0tYWBgwvD8EDFHyySefyOeffy4iIs3NzVJQUCAiIrt375bvv//el6F51Q8//CDbtm0TEZHa2lrZtGmTjyPynoMHD8rOnTtFRKSrq0tyc3OloqJCPvvsM98G5mX9/f3yxhtvDFuWn58vra2tYpqmbNiwQerq6nwU3d2xY8cOKS8vD9j+fODAAXn99dflzTffFBGRjRs3Sk1NjYiIbN++XSorK6Wurk4KCwvFNE1pb2+X1atX+zJkj7h5u9etWycXLlwQEZFvv/1WysrKRERkzZo10tnZ6asw7wr9RA4888wzBF+flH5wcND9vL6+ngsXLvDll18SHx/PsmXL3PfQB4LbVe0LNI8++ijp6enu361WK/X19bS0tHDs2DFsNhs5OTnus/ZA0dDQQH9/P2+//TaDg4MsWbIEp9PpHnlJTk6mpqaGuLg4H0fqHXV1dTQ1NfHyyy+zYcOGgOzPEydOJD8/n23btgGu49bQSENKSgrV1dXExMSQnJyMYRiMHz+ewcFBurq6iIz034lCbt7u1157jXHjXDObDR3HTdOkra2N999/n87OTubPn09mZqYvw/aKey6RHzx4kC+++GLYspUrVxIfH09HRwdbt24lJycHgKSkJObNm8eECRPYsWMH5eXlLFy40AdRe8ftqvYFmqFbGu12O5s3b+bFF1/E4XCQlZVFXFwc+/btY8+ePWRnZ/s4Us8KCQnh2WefJSsri9bWVt55551h+zw0NJRLly75MELv2r9/Py+88AIQuP05PT39v/bhUOnooWqZdrudiIgb8z8MLffnRH7zdg8l8draWr755hvWr19Pf38/CxcuZNGiRZimyfr165k2bRoPPvigr8L2insukWdmZt7yjKyxsZF3332X5cuXu89m58+f7640l5qaSmVl5V2N1dtuV7UvEF2+fJni4mIWLFhARkYGPT097v2blpZGaWmpjyP0vEmTJmGz2TAMg5iYGMLDw+nu7nav7+vrC7zvC6/r6emhubmZ2bNnA4Hfn4f8ev6HoWqZN/f1m0/iA8XRo0fZt28fq1evJjIyEtM0efrpp90zas6ePZuGhoaAS+R61TrQ1NTE5s2bycvLIyXFNUOOiJCfn8+VK1cAAnL48XZV+wJNR0cHRUVFLFu2zH0iV1RUxPnz5wE4efJkwO1fgEOHDrFr1y4Arl69Sn9/P6GhobS1tSEiVFdXM2PGDB9H6R2nT59mzpw5wL3Rn4c89NBDnDp1CoCqqipmzJhBYmIi1dXVmKbJ5cuXERG//jR+K0eOHOHrr7+msLCQiRMnAtDS0sK6deswTROn08mZM2eYOnWqjyP1vHvuE/mtfPzxxzgcDsrKygBXDfmCggJyc3MpLi5m1KhRPPDAA2RlZfk2UA9LS0vjp59+Ys2aNe6qfYFq//79dHd3s3fvXvbu3QtAdnY2ZWVlBAUFERUVxSuvvOLjKD0vMzOTkpIS1q5di2EYrFy5EsMw2Lp1K6ZpkpSUxMMPP+zrML2ipaXFfUA3DCPg+/OQ7Oxstm/fjtPpZPLkyaSnp2OxWEhMTHT39RUrVvg6TI8yTZOdO3cyfvx4iouLAZg5cyZLly4lIyODt956C6vVyhNPPEFsbKyPo/U8reymlFJK+TEdWldKKaX8mCZypZRSyo9pIldKKaX8mCZypZRSyo9pIldKKaX8mCZypZRSyo9pIldKKaX8mCZypZRSyo/9Bz7cRe3iVGMcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_train, kde=True)\n",
    "plt.title('Distribution of Scores')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Hours VS Scores')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(X_train, y_train)\n",
    "plt.title('Hours VS Scores')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Task 3 : Model Development\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting simple linear regression to the training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>17.302210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>34.055294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.445267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  17.302210\n",
       "1      27  34.055294\n",
       "2      69  75.445267"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predicting the test set results\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Task 4 : Visualizing the Results\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualising the training set results\n",
    "plt.scatter(X_train, y_train, color = 'red')\n",
    "plt.plot(X_train,  regressor.predict(X_train), color= 'blue')\n",
    "plt.title('Hours VS Scores (Training set)')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualising the test set results\n",
    "plt.scatter(X_test, y_test, color = 'red')\n",
    "plt.plot(X_train,  regressor.predict(X_train), color= 'blue')\n",
    "plt.title('Hours VS Scores (Test set)')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Task 5 : Model Evaluation\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = X_test.shape[1]\n",
    "n = len(X_test)\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 = 0.9298077534821835 \n",
      "Adjusted R2 = 0.859615506964367\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from math import sqrt\n",
    "\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "adj_r2 = 1-(1-r2)*(n-1)/(n-k-1)\n",
    "\n",
    "print('R2 =', r2, '\\nAdjusted R2 =', adj_r2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Task 6 : Predicting Future Data\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hours = 9.25 \n",
      "Prediction = [93.67656425]\n"
     ]
    }
   ],
   "source": [
    "Hours = 9.25\n",
    "my_pred = regressor.predict([[Hours]])\n",
    "\n",
    "print('Hours =', Hours, '\\nPrediction =', my_pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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