EXERCISE-3_LINEAR_REGRESSION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=4, linewidth=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>S. No.</th>\n",
       "      <th>Percentage in Grade 10</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>62.00</td>\n",
       "      <td>270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>76.33</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>72.00</td>\n",
       "      <td>240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>60.00</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>61.00</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>55.00</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>70.00</td>\n",
       "      <td>260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>68.00</td>\n",
       "      <td>235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>82.80</td>\n",
       "      <td>425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>59.00</td>\n",
       "      <td>240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>58.00</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>60.00</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>66.00</td>\n",
       "      <td>428000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>83.00</td>\n",
       "      <td>450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>68.00</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>37.33</td>\n",
       "      <td>240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>79.00</td>\n",
       "      <td>252000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>68.40</td>\n",
       "      <td>280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>70.00</td>\n",
       "      <td>231000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>59.00</td>\n",
       "      <td>224000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>63.00</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>50.00</td>\n",
       "      <td>260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>69.00</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>52.00</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>49.00</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>64.60</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>50.00</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>74.00</td>\n",
       "      <td>218000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>58.00</td>\n",
       "      <td>360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>67.00</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>75.00</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>60.00</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>55.00</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>78.00</td>\n",
       "      <td>330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>50.08</td>\n",
       "      <td>265000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>56.00</td>\n",
       "      <td>340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>68.00</td>\n",
       "      <td>177600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>52.00</td>\n",
       "      <td>236000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>54.00</td>\n",
       "      <td>265000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>52.00</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>76.00</td>\n",
       "      <td>393000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>64.80</td>\n",
       "      <td>360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>74.40</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>74.50</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>73.50</td>\n",
       "      <td>360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>57.58</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>68.00</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>69.00</td>\n",
       "      <td>270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>66.00</td>\n",
       "      <td>240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>60.80</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    S. No.  Percentage in Grade 10  Salary\n",
       "0        1                   62.00  270000\n",
       "1        2                   76.33  200000\n",
       "2        3                   72.00  240000\n",
       "3        4                   60.00  250000\n",
       "4        5                   61.00  180000\n",
       "5        6                   55.00  300000\n",
       "6        7                   70.00  260000\n",
       "7        8                   68.00  235000\n",
       "8        9                   82.80  425000\n",
       "9       10                   59.00  240000\n",
       "10      11                   58.00  250000\n",
       "11      12                   60.00  180000\n",
       "12      13                   66.00  428000\n",
       "13      14                   83.00  450000\n",
       "14      15                   68.00  300000\n",
       "15      16                   37.33  240000\n",
       "16      17                   79.00  252000\n",
       "17      18                   68.40  280000\n",
       "18      19                   70.00  231000\n",
       "19      20                   59.00  224000\n",
       "20      21                   63.00  120000\n",
       "21      22                   50.00  260000\n",
       "22      23                   69.00  300000\n",
       "23      24                   52.00  120000\n",
       "24      25                   49.00  120000\n",
       "25      26                   64.60  250000\n",
       "26      27                   50.00  180000\n",
       "27      28                   74.00  218000\n",
       "28      29                   58.00  360000\n",
       "29      30                   67.00  150000\n",
       "30      31                   75.00  250000\n",
       "31      32                   60.00  200000\n",
       "32      33                   55.00  300000\n",
       "33      34                   78.00  330000\n",
       "34      35                   50.08  265000\n",
       "35      36                   56.00  340000\n",
       "36      37                   68.00  177600\n",
       "37      38                   52.00  236000\n",
       "38      39                   54.00  265000\n",
       "39      40                   52.00  200000\n",
       "40      41                   76.00  393000\n",
       "41      42                   64.80  360000\n",
       "42      43                   74.40  300000\n",
       "43      44                   74.50  250000\n",
       "44      45                   73.50  360000\n",
       "45      46                   57.58  180000\n",
       "46      47                   68.00  180000\n",
       "47      48                   69.00  270000\n",
       "48      49                   66.00  240000\n",
       "49      50                   60.80  300000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mba_data = pd.read_csv('MBA Salary.csv')\n",
    "mba_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 3 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   S. No.                  50 non-null     int64  \n",
      " 1   Percentage in Grade 10  50 non-null     float64\n",
      " 2   Salary                  50 non-null     int64  \n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 1.3 KB\n"
     ]
    }
   ],
   "source": [
    "mba_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>const</th>\n",
       "      <th>Percentage in Grade 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>76.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>72.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>61.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const  Percentage in Grade 10\n",
       "0    1.0                   62.00\n",
       "1    1.0                   76.33\n",
       "2    1.0                   72.00\n",
       "3    1.0                   60.00\n",
       "4    1.0                   61.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import statsmodels.api as sm\n",
    "#X = sm.add_constant( mba_data['Percentage in Grade 10'] )\n",
    "#X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= mba_data[['Percentage in Grade 10']]\n",
    "\n",
    "Y = mba_data['Salary']\n",
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split( X ,Y,train_size = 0.8,random_state = 0 ) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor=model.fit(train_x,train_y)\n",
    "regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([238160.4747, 244928.6225, 238160.4747, 261172.1773, 285537.5095, 292305.6573, 224624.179 ,\n",
       "       244928.6225, 275385.2877, 248312.6964])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=regressor.predict(test_x)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59551.05148012418"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "me=mean_absolute_error(test_y,y_pred)\n",
    "me"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Percentage in Grade 10', ylabel='Salary'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=X,y=Y,color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZiDFTVdfWS66HtY5hib44PvWuuXz5t1tstV0TtSZMHiIyQ0ROBVJEZJWInOK/nY/ThGVM2Khr6yFnmpcmGc+iGRm8+6RCbv7F6zZx0ESlyWoelwL/gTOB7w7gP/23zwFfcjc0Y4JT19YzLdvPBuo9q5zVdr6xbpvHkRgTehMmD1X9L1W9APi4ql4w4naNqj4xTTEaE5D69l6yksMnecSJcPP581i/u4G1rx32OhxjQmrCeR4i8hFV/TlQLiKfG/28qt7hWmTGBMnL2eXjSU308S8XLeT2p7ezrCSLZSVZXodkTEhM1myV5v83HcgY42ZM2Kht65627WeDUZKdwkfPmM0tv3ydnv5Br8MxJiQmrHmo6v3+f78xPeEYc+LqWns5Z3541TyGnT0/n9cPN/P/nt/NF69Y7HU4xkxZoJMEfyAimSKSICIviEijiHzE7eCMCUZ9e68ne3kE6qNnzGbtxiPsa+jwOhRjpizQeR6XqGobcBXOUuoLgX93LSpjgjQ0pBzr9GYvj0BlpyZyxckz+P7vd3odijFTFmjyGP46dwXwmKrafpsmrDR39ZGSGE9CvLezyydz6dIZVB5qZmdtm9ehGDMlgf6mPSUiO4EK4AURKQBs6qwJG7VtPeSlJXkdxqSSfPFcurSI+9bv8zoUY6YkoOShqrcBZwIV/uXXO4Fr3QzMmGDUh8nSJIF496Ii/rijnsaOXq9DMeaEBbNf52Kc+R4jX/NoiOMx5oTUtfWQFcad5SOlJ/tYXZ7DrzdVsea8eV6HY8wJCXS01X/jLFNyDrDaf7PVdE3YqGvrISslMpIHwLkLC/jVxiM4e6IZE3kCrXlUAEs0yE+6iFyGs2lUPM6y7t8b9XwW8HOgzB/Lf6jqz4J5D2MAqlt6wnqY7miLijLo6htkW3WbzTo3ESnQDvOtwIxgTiwi8cDdwOXAEuA6EVkyqthngO2qugI4H/hPEYmMhmsTVmrDaEXdQIgIZ8zN5anN1V6HYswJCTR55APbReQ5EVk3fJvkNacBe1V1v6r2AWs5vpNdgQwREZwlUJqAgSDiNwZwmq0ipcN82OryXJ7ZUmNNVyYiBdps9fUTOHcJcGTE4yrg9FFl7gLWAdU4a2X9o6oet/mBiNwE3ARQVlZ2AqGYaBdOe3kEak5+Gj0DQ+yt72BBkS0VZyJLoEN1XwQOAgn++xuB1yd5mYx1qlGPLwXeBGYCK4G7RCRzjPd/QFUrVLWioKAgkJBNDOkfHKK9ZyCslmMPhIhwSmk2L+ys9zoUY4IW6GirTwG/Bu73HyoBnpzkZVVA6YjHs3BqGCPdCDyhjr3AAeCkQGIyZlh9ey/ZqQnExY31fSW8LS/N5o/b67wOw5igBdrn8RngbKANQFX3AIWTvGYjsEBE5vg7wT+E00Q10mHgQgARKQIWAfsDjMkYwNnHIxJml49lSXEm26rb6Oi1rj4TWQJNHr3+Tm8A/BMFJ+zlU9UB4BbgOWAH8CtV3SYia0Rkjb/Y7cBZIrIFeAH4gqo2BvtDmNjm9HdEVpPVsOSEeBYUpfPq/mNeh2JMUALtMH9RRL4EpIjIxcDNwFOTvUhVnwGeGXXsvhH3q4FLAg/XmOOF4w6CwVg8I5O/7W3kwsVFXodiTMACrXncBjQAW4BP4ySE/+tWUMYEo7a1h+wIml0+2pKZmfxtr9U8TGQJqOahqkMi8iTwpKo2uBuSMcGpbu2mLDfV6zBO2Nz8NA43ddHa3R9RS6yY2DZhzUMcXxeRRmAnsEtEGkTkq9MTnjGTq2ntITfC5niM5IuPY2FROpUHbZscEzkma7b6F5xRVqtVNU9Vc3Em+p0tIv/qdnDGBKKuLbKTB8CCogxeO2DJw0SOyZLHDcB1qnpg+ICq7gc+4n/OGE+pKvXtvRGfPBYWZfCa1TxMBJkseSSMNXTW3+9hjbPGcy1d/ST54kjyxXsdypTML0hnR00bvQODXodiTEAmSx59J/icMdOiprWHvAivdQCkJMYzMyuFHTXtXodiTEAmG221QkTaxjguQLIL8RgTlNq2bvLSI3N2+WjzCtN5/VAzK0uzvQ7FmElNWPNQ1XhVzRzjlqGq1mxlPFfTGlmbQE1kbn4arx9u9joMYwIS6CRBY8JSdUs3ORE8u3ykeQXpbD7S4nUYxgTEkoeJaEdbuiNuH4/xlGSncKyzj5Yu60404c+Sh4loNS3R0WEOEBcnzC1IY8vRVq9DMWZSljxMRKuJ4OXYx1Kel2ZNVyYiWPIwEUtVqWvrIS89Omoe4GxNu7mqxeswjJmUJQ8TsVq6+kmIjyM5IbInCI40Jy+NrUfHGh1vTHix5GEiVnVrNwUZ0dNkBVCUlUxbdz9NndZpbsKbq8lDRC4TkV0isldEbhunzPki8qaIbBORF92Mx0SXaOosHxYnwpz8NLZXW+3DhDfXkoeIxAN3A5cDS4DrRGTJqDLZwD3ANaq6FPiAW/GY6FPd2h3xCyKOpSwvja3VNuLKhDc3ax6nAXtVdb9///O1wLWjylwPPKGqhwFUtd7FeEyUqWqOnjkeI83OTeUtG3FlwpybyaMEODLicZX/2EgLgRwRWS8im0RkzGXeReQmEakUkcqGBtvI0DiqmrvIj5J1rUYqz09jW401W5nw5mbykDGO6ajHPuBU4ErgUuArIrLwuBepPqCqFapaUVBQEPpITUSqbukhPwprHjOzk6lt7aGzd8DrUIwZl5vJowooHfF4FlA9RplnVbXTv2/IS8AKF2MyUaS6pTuq5ngM88XFUZabys5aW57dhC83k8dGYIGIzBGRROBDwLpRZX4HvEtEfCKSirPF7Q4XYzJRon9wiKbOvqjs8wAozU1hhzVdmTA22X4eJ0xVB0TkFuA5IB54WFW3icga//P3qeoOEXkWeAsYAh5S1a1uxWSiR21rDzlpifjionOqUmlOGlttjSsTxlxLHgCq+gzwzKhj9416/EPgh27GYaLP0ZZuCqNsguBIs/NS+e0bR70Ow5hxRefXNhP1jjZ3R90EwZHKclPZ19DB0NDoMSbGhAdLHiYiVTV3ReUEwWFpST4ykn0cauryOhRjxmTJw0Skw01d5EdxsxXA7Lw06zQ3YcvVPg9jXDE4wFD9TlbPaKd4ezMJ3Q34+lqJG+wBYCg+icGETPpSCuhLm0lPxmy6s+Yz5Ev2OPDgzMpxRlxdcXKx16EYcxxLHib8DQ3C0ddhzx/gwItQ+xZf6E/HN1SKpBcykJjFoC+VgaRsAGRogPj+DtK7qkk4+mcSu2pJ6qymJ6OM9oJTaZ1xFq3FZzOQnOvtzzWJspxU29vDhC1LHiZ81W2D1x+FbU9AQhqUnAqLrmDw7H/l336+jc+vWhTwUF0ZGiCp/RCpLbuZsfvnzHvlS3RlL6Cx/CqOzb6S/tQil3+Y4JXlpfKryiOTFzTGA5Y8THgZGoSdT8PLP4HmQzDv3XDhNyD77cUKmjp6SU+MD2qOh8b56MmaR0/WPJpmX44M9pHWtI2smr9RuvlHdOSvoG7h9TTPuhCNS3DjJwtaUUYyzV19tPX0k5kcHjEZM8yShwkPQ0NODWP9dyE+ERZfA2VnQtzxuwTWt/VOeWa5xifSUbCKjoJV1C66gcz6jcx66y7mvPZ1ahZ9jLqF1zOYlDWl95iquDihLDeNXbXtrC4P7yY2E3sseRjvHdoAv/93GOyHVTfAzFUgY62r6ahv7yErJXTDdDU+idbic2gtPofk9oPkHn6Oku33Uzfvg9Qs/RT9Kd4txlmam8LOmjZLHibsWPIw3ulogGdvgwMvwakfg/JzJ0waw+rbe8lKceej25NRTvXST5PQ3Uje4WdY+buLqJv/j1QvW+NJB/usnFS22a6CJgzZPA8z/VRhy6/hntOd+9feDXPOCyhxgLOuVXaquxME+1PyqV10A/vO+C7JHYdY9bsLKdlyF3ED3a6+72hluam2Ja0JS1bzMNOruxme+izUbIYLvgz5x23fMqm6th4WFqW7ENzxBpJzqT3pRppKL6Nw368p2vULDp/yBRrnXAPi/nev0txU9vqXKYmLCyy5GjMdrOZhps+RjXDv2U5t48o7TihxgNNs5XbNY7S+tGKqlt/K0WVrKNl6D8t+/z7Sjr3l+vumJ/lIS/JxpNmWKTHhxZKHcZ8qvHIv/PIDcOqNcNpNzoiqE9DdP0jvwCDpSd5UmruzF3Fg9ddpKzqDxS/8E3M3fBFfb4ur71mel2rLlJiwY8nDuKu/B564CTY+BJf/EMrOmNLp6tp6yU1LJC7A/hFXSBwtJeez98zv4+trZeXvLqJg36+dJOmCWdm2MZQJP5Y8jHs66uFnl0NHHVz6PciYMeVT1rV1kzvNTVbjGUpIo3bRDRxe+TlmbnuApc99kJTWvSF/n9LcNLZap7kJM5Y8jDsadsGDF0DhSfCu/wMJoVmUsLZ1+vs7JtOTOZcDq79OZ97JLH32A8x68w5ksDdk5y/LS2VHje1nbsKLq8lDRC4TkV0isldEbpug3GoRGRSR97sZj5kmhzY4NY6TPwgrrg94CG4gqlu7wy55ACBxNJVewv7Tv01m/UZWPHU5mXWvhuTUxZnJNHX20t7TH5LzGRMKriUPEYkH7gYuB5YA14nIknHKfR9nr3MT6XY+A2uvg7M+66xLFWI1rT1hvYPgQHIuVcs/S8Pc97LgpVuZ9/Lnp9yhHhcnzM5NY2et1T5M+HCz5nEasFdV96tqH7AWuHaMcrcCvwHqXYzFTIfNa2HdLfDur0DJKa68RV1rd0TsINheuJp9Z36P+P4OVqy7mLwD66bUoV6aa53mJry4mTxKgJHrSVf5j/2diJQA7wXum+hEInKTiFSKSGVDQ0PIAzUhsPFh+MNX4OLbT3j+xmR6Bgbp6h8kIzky5rYO+VKpXXQDVSf/M6Wb72TxH28gqf3wCZ2rNDeVrUcteZjw4WbyGKuhe/RXrzuBL6jq4EQnUtUHVLVCVSsKCrxbpM6MY8O98NL34ZJvQ3aZa29T09JDXlqSt8N0T0B39gL2n/ZNejJms/yZaynZchcy2BfUOWbnprGtutWlCI0Jnptf4aqA0hGPZwHVo8pUAGvF+WOQD1whIgOq+qSLcZlQevkueOVuuOQ7kF7o6ltVt/aQlx7+TVZjivNxrPwq2opOZ8au/6Zg3xPsP/122orPDujlZbmp7GvoYGBwCF+8DZI03nPzU7gRWCAic0QkEfgQsG5kAVWdo6rlqloO/Bq42RJHBHn5bidxXPwt1xMHQE1LNzkpkb0pUn9KAUdW/CsNc9/Hgr/9GwtfvJnEztHfqY6XkhhPbloi+xs7pyFKYybnWvJQ1QHgFpxRVDuAX6nqNhFZIyJr3HpfM01evR823DVtiQPgSHMXeelJ0/JerhKhvbCCvWd+n0FfGiueuoJZm++cdMXe8rw0W2HXhA1Xex5V9RngmVHHxuwcV9WPuxmLCaHKn8Ff73D6OKYpcQAcbelm2Uxvd/cLJY1PomHeP9Ay81wK9z7Oqj1rObTq8zTOuXbMHRTLclPZcrSV96wqGeNsxkwvazw1wXnzMfjzd+Cib0J60bS97aAqdW090VHzGKU/pYCjJ99C1bKbmbn9QVY8fSXZVX86bmhveV4aW45ap7kJD5Y8TOC2/w7+8GW46BuQOXNa37qxvY/UBB9Jvuj9yHZnL+RgxVdpLL+S8o23s+z3/0BWzV//nkTK89PYUdOGurQAozHBiIwB88Z7e/4IT/0LXPg1yC6dtHioVTV3UZgZfbWO44jQXria9oJTyardwNwNX2IgOZcjy2+FkgtI8sVxpKmbsrxUryM1Mc6Sh5ncoQ3wxCfh/C9B3jxPQjjS3EV+FDZZjUviaC0+m9YZZ5JZ9xrlm74Lm77LJ1OvZtvh+ZTlzfE6QhPjLHmYidVshrXXwzmfg8LFnoVxuKkrcud4TIXE0TbjDNqKTietaSuX7niaGU//DOo/BhU3epbMjYneBmQzdY174Ofvh9PXwMxVnoZypKmboozQLOsekUTozDuZzQtv4cH0T0N7NTx0Ifz0Unjj59BjQ3jN9LLkYcbWWgWPXgMrr4PZZ3kaysCQUtPWTUFGDDVbjWNmVgpvtKaip34c/uFhmHcBvPlLuGMxPHY9bPm1JRIzLazZyhyvsxH+62pYdAXMv9jraKht7SYrOYEEW5aDtCQfSfFx1LT2MDMrBWaf7dx6O+Dwy/DaA7DuViipgJOugPkXQd78kO6pYgxY8jCj9bTBo++BWafBkvd4HQ0AB491UZQVw01Wo5TkpLC/odNJHsOS0mHBJc6tvwuq34D9653JnBIPc86Duec5iSa7zJKJmTJLHuZt/d3wyw9CThms/LDX0fzdwWOdFMbSSKtJzMhMZnddO+fMzx+7QELq2zUSVacJsvYt2Pw4/OH/QlwClJ0OZWdB6WqYsRziI3vNMDP9LHkYx2A//OoG8CXDaZ8Oq2+mBxo7o2pZkqmalZ3CS3sC3NdGxJmXk10KJ13pJJP2GqjfDgdedJq52quhcCmUng6lp8Gs1ZBlS6CYiVnyMDA0BL9dAz2tcP4XQcKnb0FRDjV2cdHi6VsKJdwVZ6dwtKWbvsEhEoPtBxJxVgfInOn0hwD0dcGx3dC4G169D576rPMlonQ1zD4HZp8JRcvGXG/LxC5LHrFOFf73c84fjgu/BnHh9ZE41tEHAhlJ4RWXlxLi4yjISOZAYyeLijKmfsLEVChe6dzg7dpJww6ndvLqvdB1zKmZzLsQ5p7vzPkJo9qpmX72GxnLVOH5r8LhDc5Ch77w61fY39jJzOwUxP5QvUNJdgq769pDkzxGG1k7mXehc6y7Geq2wsG/woafwNCgMxLvpCud4cIJKROf00QdSx6x7MXvw86n4OJvO98+w9De+g5mxMKaVkEqyU5hR00bVy+fpgUqU3Kg/F3OTRXaqqFqo/MZeuJTTm3k5PfDwssskcQISx6x6m8/dmYmX/odSM70Oppx7alrZ0VpttdhhJ2y3FSe31GHogjTXCsTcTrUs0pg6XucvrIjr8LLP4F1/+zMDzrlo85oL6sxRi1Xe0ZF5DIR2SUie0XktjGe/7CIvOW/vSwiK9yMx/i9ej+8ci9cfLvzjTJMDapy4JjTbGXeKTMlAV+cUN3S43UokJzlzC+56BtwzV2QmAa/uwV+vNL5ktLV5HWExgWuJQ8RiQfuBi4HlgDXiciSUcUOAOep6nLgduABt+Ixfht/Cn+5Ay7+JqSNM08gTBxt7iY9MYG0RKsgj6UsL40dNe1eh/FOqbmw9L1w9Y/hjM/AgZfgzuXw5M1Qt93r6EwIuVnzOA3Yq6r7VbUPWAtcO7KAqr6sqs3+h68As1yMx1T+zGmjvuRbkDHD62gmtauunZIcq3WMpywnha3hurOgiDMi6+zPwnvvc4Z//9dVzuoFB/963C6JJvK4mTxKgCMjHlf5j43nE8Dvx3pCRG4SkUoRqWxoCHBylHmnjQ/D+u85TVURkDgAdtS0McuSx7jK89PYXtOKEuZ/iJOzYPk/wvsedOaL/PbT8NBFsPePlkQimJvJY6yesjE/KSJyAU7y+MJYz6vqA6paoaoVBQUFIQwxRrxyn1PjuPj2ad8+dip21rZTlhOeo8DCQba/3+Noc7fXoQQmPhEWXgrX3A1zL4D//Td48N2w7/j92k34c7MxuQoYuV/pLKB6dCERWQ48BFyuqsdcjCc2/eUO2PigM6oqvdDraAJW395L38BQbG4AFSARoTwvjS1HW5kVSUk2Lt5ZpLH8HDj0V1j3WWfk1sW3O7PaTURws+axEVggInNEJBH4ELBuZAERKQOeAD6qqrtdjCX2qMIfvwGbHoFLIitxAGyrbmVOXqpNDpzEnIJ03jjc4nUYJybOv9rvNT9x1tNaez388kPQYH8KIoFryUNVB4BbgOeAHcCvVHWbiKwRkTX+Yl8F8oB7RORNEal0K56YMjTorE+083+dGkeYj6oay+YjrczOS/M6jLA3Jz+NXXXt9A0OeR3KiYuLd4b6vudeyCyGn17sfH476r2OzEzA1XkeqvqMqi5U1Xmq+m3/sftU9T7//U+qao6qrvTfKtyMJyb0d8PjH4XaLXDJ7U5nZYQZUmVrdStzC9K9DiXspSTEMyMzme3VUbB7oC8Jlr7PSSI9rXDXanjxh85n2oSd8Fk+1UxdZyM8cpWzGdC7v+Ls6xCB9jd2kpoYT1aK7TERiHmF6VQeiqKJeEkZUPEJuPyHcGA9/PgUZ3td61QPK5Y8okXDbnjwAsibB+f8a0Rv7rPpUDPzrNYRsEVFGVQebGYo2v64ZhbDebfBWf8ML/7AGZl1ZKPXURk/Sx7RYM/z8PClTpV/1UfDaj+OE7HxYBMLiyx5BCo/PYmE+Dj2NnR4HYo7ZiyDK34Ic86FtdfB/9zo7I5oPBXZf2VinSq89B/ORk7n3/b25j4RrLath5au/sgaehoGFhdn8PK+KB7pLnHO5/vae5y+kXvPghe+Cb1RmjAjgCWPSNXdDI99CLb+Gq74DygcvWxYZNqwr5HFxRnE2RDdoCydmcWGvY0MRlvT1WgJKbDyw3DVnVD9Jvx4FVQ+4owwNNPKkkckOrIR7nsXxCc5czgicCjuWBTlpd2NLCkO3yXiw1V+ehLpyQnhu9ZVqKUVOH17538RNj0M95wBu5+zTvVpZMkjkgz2w5+/B7/8oNO3cdqnIrpjfLR9DZ30DgxRak1WJ+TkWVm8sCPG5kbkL4CLv+WsnfX7z8PDl8GR17yOKiZY8ogU9Tuc0SZ7n4cr74DZZ3kdUcg9v72OFbOybVb5CTq5JIstR1tp7u7zOpTpJeLsr37Vj6D0NHj8I/CLD0LtVq8ji2qWPMJdfw/86dvON6o558KFX4uaZqqROnoHeO1AEyvLIm9SY7hI9sWzZGYmf9hW53Uo3hg5Uz27DB69BtZ+BOq2eR1ZVLLkEa5UYdezcPfpcHiD00G48LKo3dbz2a21LJqRQXpS9DTDeeG08lye315Hd38MdyDHJ8KSa+E990N6AfzX1c6aWUc3eR1ZVLHkEY5q3nK+Nf3+36HiRjjvC1FZ2xjW1TfAs9tqOXNuntehRLz89CRm56Xy3LZar0PxXkKyM/fpvfc7NZHHroOfXeHMixqK4LXAwoTt7xlOGnbBn7/t7LR28gfgnM9BXPT/F63bXMP8gnTy05O8DiUqnLuggP/ecIgLTyoiIzn6Pz+T8iXD4qth0eXOtrjPfhF0CE5fAyuvc5ZDMUGzmkc4OLLR+Vb08KWQkgvvuQ9OuiomEkdtWw/Pb6/jvIW2yVeo5KcnsWRmJr989bDXoYSXOB/Me7cz4GT1J2HHOrhjCay7Fao22TDfIEX/X6dw1d8N256E1+6Hjjo46Wp47z85Ve0YMajKPev3cda8PDJtEcSQOm9RAQ+8uI+3juayvCTb63DCiwjMONm5dR2DvS/Arz7qLCS68npY9g+QM9vrKMOeJY/pNDQER16BzWth+5OQvwgWXQWzKpyRIjHm8Y1H6B8c4rQ5uV6HEnWSffFcvaKEu/+0l++872Ty0qxJcEypebD8g04zcf12OPAi/O1HkDMHlr4XTroS8ud7HWVYEo2wqlpFRYVWVkbQnlH9PU4fxs6nnc2ZkjKc7TfnXhDVneCT+cP2Wp58s5qPn1lOWpJ9h3HLK/sb2V7TzteuXkqm9X8EZmjA2Q/nyCvOhMPENJh/Mcy/EGafDcmRuQKCiGwK5Z5JljxCrb8bqt+AQy/D/hfhaKWzTPrMU6DsTMia5XWEnhpS5YnXj/LCjjo+fMZsclJtj3I3qSrrdzWwr6GDL1x2EjOyYqdZNCR0CJoPOsN8a7dAww7IW+BM0i09HUpOgezZETGEPqKSh4hcBvwIiAceUtXvjXpe/M9fAXQBH1fV1yc6Z9gkD1Vn86WGnc7s75rNUPMmHNsLOeVQsBiKljrtqom2nSrAkeYufvqXA3T1D/LeVSVkJls/x3SpPNTEX3Y38oGKWVy4uAhfXPj/sQtLg/3QuMv5nT+2x9lHZ6j/7d/1wqVQsAjy5jtNYmGUVCImeYhIPLAbuBioAjYC16nq9hFlrgBuxUkepwM/UtXTJzrvtCQPVehtc5JDRz101EJ7LbQcgZZD0HwIWg4C4nSsZc5y/s2b77SV+qx9eVhn3wBbjrby4q4G9tR1cNb8PFaX59qquR6ob3dGtrV293PRkkJOK89jZnYygv1fTEl3MzTth+YD0HYUWo9C6xHn70hWqTPHJLsMskogoxjSC52FHVPznNs0/b2IpORxJvB1Vb3U//iLAKr63RFl7gfWq+pj/se7gPNVtWa8804peRx+FV78PvR1wkA39HVBX4fzuLcdGONaxPkgNRdS/P/R6YWQXgQZMyJyf/Dp0DMwyM/+eoDDzc7e00m+OObmpzG3IN2+8YaBho5e9ta1U9/hrIFVmJHEzefPI8kXe4M2XNXb4Xzx7KiHzgZnZFd3s3PrG2cfkrgEp6UiKd0Z/ZWQAr4U574vCVb8o9ORfwJCnTzc7EErAY6MeFyFU7uYrEwJ8I7kISI3ATf5H3b4k0zQitIkf1amjDsGb0gZGlIGB5VB1ZGZpAXYfyJveZymbvXlpshASE4WvgRfotO4HuC3k6auQXJT7Y/XWFy9Nv5VKH96R1+3O2/gnmj5XYoT4uKF+Lg44gUEmsct29T9q8YDLXpoklPmA41jHA/p+GM3k8dYXzFH/yEJpAyq+gDwQCiC8pqIVFa3D4Us+0cLEamsbu236zIGuzZjs9+lsYlIZShrGONxc4Z5FVA64vEsoPoEyhhjjAkzbiaPjcACEZkjIonAh4B1o8qsA24QxxlA60T9HcYYY8KDa81WqjogIrcAz+EM1X1YVbeJyBr/8/cBz+CMtNqLM1T3RrfiCSNR0fzmArsu47NrMza7LmOblusScZMEjTHGeM9W1TXGGBM0Sx7GGGOCZskjQCLysIjUi8jWEcdyReR5Ednj/zdnxHNfFJG9IrJLRC4dcfxUEdnif+7H/iVaEJEkEXncf/xVESkf8ZqP+d9jj4h8bJp+5ICISKmI/FlEdojINhH5rP94TF8bEUkWkddEZLP/unzDfzymr8swEYkXkTdE5Gn/Y7sugIgc9P9Mb4pIpf9YeF4bVbVbADfgXOAUYOuIYz8AbvPfvw34vv/+EmAzkATMAfYB8f7nXgPOxJnj8nvgcv/xm4H7/Pc/BDzuv5+LM0MxF8jx38/x+nqMuAbFwCn++xk4S9IsifVr4/8Z0v33E4BXgTNi/bqMuD6fA34JPG2/S++4LgeB/FHHwvLaeH6xIukGlPPO5LELKPbfLwZ2+e9/EfjiiHLP+f8ji4GdI45fB9w/soz/vg9nhqiMLON/7n6cNcI8vx7jXKPf4axnZtfm7bhSgddxVliI+euCM5/rBeDdvJ08Yv66+GM6yPHJIyyvjTVbTU2R+uel+P8t9B8fb9mVEv/90cff8RpVHQBagbwJzhV2/FXgVTjfsmP+2vibZt4E6oHnVdWui+NO4PPA0Ihjdl0cCvxBRDaJsywThOm1sd1h3DHesisTLcdyIq8JGyKSDvwG+BdVbZPxV82NmWujqoPAShHJBn4rIssmKB4T10VErgLqVXWTiJwfyEvGOBZ112WEs1W1WkQKgedFZOcEZT29NlbzmJo6ESkG8P9b7z8+3rIrVf77o4+/4zUi4gOygKYJzhU2RCQBJ3H8QlWf8B+2a+Onqi3AeuAy7LqcDVwjIgeBtcC7ReTn2HUBQFWr/f/WA78FTiNcr43XbXyRdOP4Po8f8s6OrB/47y/lnR1Z+3m7I2sjTsfpcEfWFf7jn+GdHVm/8t/PBQ7gdGLl+O/nen0tRlwDAR4F7hx1PKavDVAAZPvvpwB/Aa6K9esy6hqdz9t9HjF/XYA0IGPE/ZdxvnCE5bXx/IJFyg14DGep+H6cLP0JnLbCF4A9/n9zR5T/Ms7oh134Rzr4j1cAW/3P3cXbs/yTgf/BWarlNWDuiNf8k//4XuBGr6/FqOtyDk719i3gTf/tili/NsBy4A3/ddkKfNV/PKavy6hrdD5vJ4+Yvy7AXJxksBnYBnw5nK+NLU9ijDEmaNbnYYwxJmiWPIwxxgTNkocxxpigWfIwxhgTNEsexhhjgmbJw5gxiMiX/avhvuVf4fT0Cco+IiLvn874jPGaLU9izCgicibOhL5TVLVXRPKBxBCe36fOukLGRCyreRhzvGKgUVV7AVS1UZ31hr4qIhtFZKuIPDC8R8JI45URkfUi8h0ReRH4sogc8C/rgohk+vdxSJjOH9KYqbDkYczx/gCUishuEblHRM7zH79LVVer6jKcJUeuGuO1E5XJVtXzVPUbOGtdXek//iHgN6ra78pPY4wLLHkYM4qqdgCnAjcBDcDjIvJx4AL/7mtbcPaiWDrGyycq8/iI+w8BN/rv3wj8LLQ/hTHusj4PY8agznLq64H1/kTwaZz1qipU9YiIfB1nnaC/E5Fk4J4JynSOOP/fRKTcX6uJV9WtGBNBrOZhzCgiskhEFow4tBJn4TmARv/eJWONrkoOoMxIj+IsuGm1DhNxrOZhzPHSgZ/4N3EawFll9CagBdiCs1XoxtEvUtUWEXlwojKj/AL4Fk4CMSai2Kq6xnjEPzfkWlX9qNexGBMsq3kY4wER+QlwOc7eJ8ZEHKt5GGOMCZp1mBtjjAmaJQ9jjDFBs+RhjDEmaJY8jDHGBM2ShzHGmKD9f8Xtqni4vFT8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Actual vs predicted distribution plot\n",
    "sns.kdeplot(y_pred, label=\"Predicted\", shade=True);\n",
    "sns.kdeplot(data=test_y, label=\"Actual\", shade=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.20119445806571035 \n",
      " test accuracy: -0.13516740049840825\n"
     ]
    }
   ],
   "source": [
    "# Train and test accuracy\n",
    "print(\"Accuracy:\", regressor.score(train_x, train_y),\"\\n test accuracy:\", regressor.score(test_x,test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}