Linear_Regression_Boston.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boston Housing Price Prediction using Linear-Regression, SGDRegressor and implementation of SGD\n",
    "\n",
    "#### Boston House Prices dataset\n",
    "===========================\n",
    "\n",
    "Notes\n",
    "------\n",
    "**Data Set Characteristics: **\n",
    "\n",
    "    :Number of Instances: 506 \n",
    "\n",
    "    :Number of Attributes: 13 numeric/categorical predictive\n",
    "    \n",
    "    :Median Value (attribute 14) is usually the target\n",
    "\n",
    "    :Attribute Information (in order):\n",
    "        - CRIM     per capita crime rate by town\n",
    "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
    "        - INDUS    proportion of non-retail business acres per town\n",
    "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
    "        - NOX      nitric oxides concentration (parts per 10 million)\n",
    "        - RM       average number of rooms per dwelling\n",
    "        - AGE      proportion of owner-occupied units built prior to 1940\n",
    "        - DIS      weighted distances to five Boston employment centres\n",
    "        - RAD      index of accessibility to radial highways\n",
    "        - TAX      full-value property-tax rate per  $10,000\n",
    "        - PT       pupil-teacher ratio by town\n",
    "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
    "        - LSTAT    % lower status of the population\n",
    "        - MV     Median value of owner-occupied homes in $1000's\n",
    "\n",
    "    :Missing Attribute Values: None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#importing required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#importing data into pandas dataframe\n",
    "boston_data = pd.read_csv(\"boston.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the number of data-points and columns\n",
    "boston_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
       "       'PT', 'B', 'LSTAT', 'MV'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the column names\n",
    "boston_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PT</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.199997</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>396.899994</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.900002</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>396.899994</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.099998</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>392.829987</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.700001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.799999</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.700001</td>\n",
       "      <td>394.630005</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.200001</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.700001</td>\n",
       "      <td>396.899994</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.200001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM        AGE     DIS  RAD  TAX  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.199997  4.0900    1  296   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.900002  4.9671    2  242   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.099998  4.9671    2  242   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.799999  6.0622    3  222   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.200001  6.0622    3  222   \n",
       "\n",
       "          PT           B  LSTAT         MV  \n",
       "0  15.300000  396.899994   4.98  24.000000  \n",
       "1  17.799999  396.899994   9.14  21.600000  \n",
       "2  17.799999  392.829987   4.03  34.700001  \n",
       "3  18.700001  394.630005   2.94  33.400002  \n",
       "4  18.700001  396.899994   5.33  36.200001  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the head of the data\n",
    "boston_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM     False\n",
       "ZN       False\n",
       "INDUS    False\n",
       "CHAS     False\n",
       "NOX      False\n",
       "RM       False\n",
       "AGE      False\n",
       "DIS      False\n",
       "RAD      False\n",
       "TAX      False\n",
       "PT       False\n",
       "B        False\n",
       "LSTAT    False\n",
       "MV       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null-values\n",
    "boston_data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM     506 non-null float64\n",
      "ZN       506 non-null float64\n",
      "INDUS    506 non-null float64\n",
      "CHAS     506 non-null int64\n",
      "NOX      506 non-null float64\n",
      "RM       506 non-null float64\n",
      "AGE      506 non-null float64\n",
      "DIS      506 non-null float64\n",
      "RAD      506 non-null int64\n",
      "TAX      506 non-null int64\n",
      "PT       506 non-null float64\n",
      "B        506 non-null float64\n",
      "LSTAT    506 non-null float64\n",
      "MV       506 non-null float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.4 KB\n"
     ]
    }
   ],
   "source": [
    "boston_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PT</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674030</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148862</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294863</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377487</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440002</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.074999</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200001</td>\n",
       "      <td>396.225006</td>\n",
       "      <td>16.954999</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976196</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.899994</td>\n",
       "      <td>37.970001</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976196  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX          PT           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674030   \n",
       "std     28.148862    2.105710    8.707259  168.537116    2.164946   91.294863   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377487   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440002   \n",
       "75%     94.074999    5.188425   24.000000  666.000000   20.200001  396.225006   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.899994   \n",
       "\n",
       "            LSTAT          MV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200001  \n",
       "75%     16.954999   25.000000  \n",
       "max     37.970001   50.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### observations::\n",
    "\n",
    "* The average per capita crime rate is 3.61\n",
    "* The average number of rooms per dweling is 6.28, but ranges from 3.5 to 8.78\n",
    "* The average nitic oxide concentration (in parts per 10 million) is 0.55. The neighborhood with the least NOX has      0.38, the one with the most has 0.87.\n",
    "* The average Pupil-Teacher ratio is 18 students to one teacher.\n",
    "* The average weighted distance to the five major Boston employment centers is 3.79.\n",
    "* The average percent of lower status population is 12.65%, but ranges from 7.14% to 37.97%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking for linearity of each feature with Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTAT   -0.737663\n",
      "RM       0.695360\n",
      "PT      -0.507787\n",
      "INDUS   -0.483725\n",
      "TAX     -0.468536\n",
      "NOX     -0.427321\n",
      "CRIM    -0.388305\n",
      "RAD     -0.381626\n",
      "AGE     -0.376955\n",
      "ZN       0.360445\n",
      "B        0.333461\n",
      "DIS      0.249929\n",
      "CHAS     0.175260\n",
      "Name: MV, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#checking for correlation with target\n",
    "pearson = boston_data.corr(method='pearson')\n",
    "\n",
    "corr_with_target = pearson.iloc[-1][:-1]\n",
    "# attributes sorted from the most predictive\n",
    "sorted_corr = corr_with_target[abs(corr_with_target).argsort()[::-1]]\n",
    "print(sorted_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Features like LSTAT,RM,PT,INDUS,TAX are having better correlation with Housing price(MV) and can be best features for predicting the house price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "            background-color:  #f4edf6;\n",
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       "            background-color:  #f9f2f8;\n",
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       "            background-color:  #fff7fb;\n",
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       "            background-color:  #62a2cb;\n",
       "        }    #T_598b4786_d078_11e8_b518_1831bfbb88darow13_col12 {\n",
       "            background-color:  #fff7fb;\n",
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       "            background-color:  #023858;\n",
       "        }</style>  \n",
       "<table id=\"T_598b4786_d078_11e8_b518_1831bfbb88da\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >CRIM</th> \n",
       "        <th class=\"col_heading level0 col1\" >ZN</th> \n",
       "        <th class=\"col_heading level0 col2\" >INDUS</th> \n",
       "        <th class=\"col_heading level0 col3\" >CHAS</th> \n",
       "        <th class=\"col_heading level0 col4\" >NOX</th> \n",
       "        <th class=\"col_heading level0 col5\" >RM</th> \n",
       "        <th class=\"col_heading level0 col6\" >AGE</th> \n",
       "        <th class=\"col_heading level0 col7\" >DIS</th> \n",
       "        <th class=\"col_heading level0 col8\" >RAD</th> \n",
       "        <th class=\"col_heading level0 col9\" >TAX</th> \n",
       "        <th class=\"col_heading level0 col10\" >PT</th> \n",
       "        <th class=\"col_heading level0 col11\" >B</th> \n",
       "        <th class=\"col_heading level0 col12\" >LSTAT</th> \n",
       "        <th class=\"col_heading level0 col13\" >MV</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row0\" class=\"row_heading level0 row0\" >CRIM</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col1\" class=\"data row0 col1\" >-0.2</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col2\" class=\"data row0 col2\" >0.41</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col3\" class=\"data row0 col3\" >-0.056</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col4\" class=\"data row0 col4\" >0.42</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col5\" class=\"data row0 col5\" >-0.22</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col6\" class=\"data row0 col6\" >0.35</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col7\" class=\"data row0 col7\" >-0.38</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col8\" class=\"data row0 col8\" >0.63</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col9\" class=\"data row0 col9\" >0.58</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col10\" class=\"data row0 col10\" >0.29</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col11\" class=\"data row0 col11\" >-0.39</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col12\" class=\"data row0 col12\" >0.46</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow0_col13\" class=\"data row0 col13\" >-0.39</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row1\" class=\"row_heading level0 row1\" >ZN</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col0\" class=\"data row1 col0\" >-0.2</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col1\" class=\"data row1 col1\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col2\" class=\"data row1 col2\" >-0.53</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col3\" class=\"data row1 col3\" >-0.043</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col4\" class=\"data row1 col4\" >-0.52</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col5\" class=\"data row1 col5\" >0.31</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col6\" class=\"data row1 col6\" >-0.57</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col7\" class=\"data row1 col7\" >0.66</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col8\" class=\"data row1 col8\" >-0.31</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col9\" class=\"data row1 col9\" >-0.31</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col10\" class=\"data row1 col10\" >-0.39</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col11\" class=\"data row1 col11\" >0.18</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col12\" class=\"data row1 col12\" >-0.41</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow1_col13\" class=\"data row1 col13\" >0.36</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row2\" class=\"row_heading level0 row2\" >INDUS</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col0\" class=\"data row2 col0\" >0.41</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col1\" class=\"data row2 col1\" >-0.53</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col2\" class=\"data row2 col2\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col3\" class=\"data row2 col3\" >0.063</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col4\" class=\"data row2 col4\" >0.76</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col5\" class=\"data row2 col5\" >-0.39</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col6\" class=\"data row2 col6\" >0.64</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col7\" class=\"data row2 col7\" >-0.71</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col8\" class=\"data row2 col8\" >0.6</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col9\" class=\"data row2 col9\" >0.72</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col10\" class=\"data row2 col10\" >0.38</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col11\" class=\"data row2 col11\" >-0.36</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col12\" class=\"data row2 col12\" >0.6</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow2_col13\" class=\"data row2 col13\" >-0.48</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row3\" class=\"row_heading level0 row3\" >CHAS</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col0\" class=\"data row3 col0\" >-0.056</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col1\" class=\"data row3 col1\" >-0.043</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col2\" class=\"data row3 col2\" >0.063</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col3\" class=\"data row3 col3\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col4\" class=\"data row3 col4\" >0.091</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col5\" class=\"data row3 col5\" >0.091</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col6\" class=\"data row3 col6\" >0.087</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col7\" class=\"data row3 col7\" >-0.099</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col8\" class=\"data row3 col8\" >-0.0074</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col9\" class=\"data row3 col9\" >-0.036</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col10\" class=\"data row3 col10\" >-0.12</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col11\" class=\"data row3 col11\" >0.049</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col12\" class=\"data row3 col12\" >-0.054</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow3_col13\" class=\"data row3 col13\" >0.18</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row4\" class=\"row_heading level0 row4\" >NOX</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col0\" class=\"data row4 col0\" >0.42</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col1\" class=\"data row4 col1\" >-0.52</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col2\" class=\"data row4 col2\" >0.76</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col3\" class=\"data row4 col3\" >0.091</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col4\" class=\"data row4 col4\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col5\" class=\"data row4 col5\" >-0.3</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col6\" class=\"data row4 col6\" >0.73</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col7\" class=\"data row4 col7\" >-0.77</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col8\" class=\"data row4 col8\" >0.61</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col9\" class=\"data row4 col9\" >0.67</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col10\" class=\"data row4 col10\" >0.19</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col11\" class=\"data row4 col11\" >-0.38</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col12\" class=\"data row4 col12\" >0.59</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow4_col13\" class=\"data row4 col13\" >-0.43</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row5\" class=\"row_heading level0 row5\" >RM</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col0\" class=\"data row5 col0\" >-0.22</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col1\" class=\"data row5 col1\" >0.31</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col2\" class=\"data row5 col2\" >-0.39</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col3\" class=\"data row5 col3\" >0.091</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col4\" class=\"data row5 col4\" >-0.3</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col5\" class=\"data row5 col5\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col6\" class=\"data row5 col6\" >-0.24</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col7\" class=\"data row5 col7\" >0.21</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col8\" class=\"data row5 col8\" >-0.21</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col9\" class=\"data row5 col9\" >-0.29</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col10\" class=\"data row5 col10\" >-0.36</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col11\" class=\"data row5 col11\" >0.13</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col12\" class=\"data row5 col12\" >-0.61</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow5_col13\" class=\"data row5 col13\" >0.7</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row6\" class=\"row_heading level0 row6\" >AGE</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col0\" class=\"data row6 col0\" >0.35</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col1\" class=\"data row6 col1\" >-0.57</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col2\" class=\"data row6 col2\" >0.64</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col3\" class=\"data row6 col3\" >0.087</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col4\" class=\"data row6 col4\" >0.73</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col5\" class=\"data row6 col5\" >-0.24</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col6\" class=\"data row6 col6\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col7\" class=\"data row6 col7\" >-0.75</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col8\" class=\"data row6 col8\" >0.46</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col9\" class=\"data row6 col9\" >0.51</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col10\" class=\"data row6 col10\" >0.26</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col11\" class=\"data row6 col11\" >-0.27</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col12\" class=\"data row6 col12\" >0.6</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow6_col13\" class=\"data row6 col13\" >-0.38</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row7\" class=\"row_heading level0 row7\" >DIS</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col0\" class=\"data row7 col0\" >-0.38</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col1\" class=\"data row7 col1\" >0.66</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col2\" class=\"data row7 col2\" >-0.71</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col3\" class=\"data row7 col3\" >-0.099</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col4\" class=\"data row7 col4\" >-0.77</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col5\" class=\"data row7 col5\" >0.21</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col6\" class=\"data row7 col6\" >-0.75</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col7\" class=\"data row7 col7\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col8\" class=\"data row7 col8\" >-0.49</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col9\" class=\"data row7 col9\" >-0.53</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col10\" class=\"data row7 col10\" >-0.23</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col11\" class=\"data row7 col11\" >0.29</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col12\" class=\"data row7 col12\" >-0.5</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow7_col13\" class=\"data row7 col13\" >0.25</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row8\" class=\"row_heading level0 row8\" >RAD</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col0\" class=\"data row8 col0\" >0.63</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col1\" class=\"data row8 col1\" >-0.31</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col2\" class=\"data row8 col2\" >0.6</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col3\" class=\"data row8 col3\" >-0.0074</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col4\" class=\"data row8 col4\" >0.61</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col5\" class=\"data row8 col5\" >-0.21</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col6\" class=\"data row8 col6\" >0.46</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col7\" class=\"data row8 col7\" >-0.49</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col8\" class=\"data row8 col8\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col9\" class=\"data row8 col9\" >0.91</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col10\" class=\"data row8 col10\" >0.46</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col11\" class=\"data row8 col11\" >-0.44</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col12\" class=\"data row8 col12\" >0.49</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow8_col13\" class=\"data row8 col13\" >-0.38</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row9\" class=\"row_heading level0 row9\" >TAX</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col0\" class=\"data row9 col0\" >0.58</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col1\" class=\"data row9 col1\" >-0.31</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col2\" class=\"data row9 col2\" >0.72</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col3\" class=\"data row9 col3\" >-0.036</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col4\" class=\"data row9 col4\" >0.67</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col5\" class=\"data row9 col5\" >-0.29</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col6\" class=\"data row9 col6\" >0.51</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col7\" class=\"data row9 col7\" >-0.53</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col8\" class=\"data row9 col8\" >0.91</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col9\" class=\"data row9 col9\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col10\" class=\"data row9 col10\" >0.46</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col11\" class=\"data row9 col11\" >-0.44</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col12\" class=\"data row9 col12\" >0.54</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow9_col13\" class=\"data row9 col13\" >-0.47</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row10\" class=\"row_heading level0 row10\" >PT</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col0\" class=\"data row10 col0\" >0.29</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col1\" class=\"data row10 col1\" >-0.39</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col2\" class=\"data row10 col2\" >0.38</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col3\" class=\"data row10 col3\" >-0.12</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col4\" class=\"data row10 col4\" >0.19</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col5\" class=\"data row10 col5\" >-0.36</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col6\" class=\"data row10 col6\" >0.26</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col7\" class=\"data row10 col7\" >-0.23</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col8\" class=\"data row10 col8\" >0.46</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col9\" class=\"data row10 col9\" >0.46</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col10\" class=\"data row10 col10\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col11\" class=\"data row10 col11\" >-0.18</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col12\" class=\"data row10 col12\" >0.37</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow10_col13\" class=\"data row10 col13\" >-0.51</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row11\" class=\"row_heading level0 row11\" >B</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col0\" class=\"data row11 col0\" >-0.39</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col1\" class=\"data row11 col1\" >0.18</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col2\" class=\"data row11 col2\" >-0.36</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col3\" class=\"data row11 col3\" >0.049</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col4\" class=\"data row11 col4\" >-0.38</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col5\" class=\"data row11 col5\" >0.13</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col6\" class=\"data row11 col6\" >-0.27</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col7\" class=\"data row11 col7\" >0.29</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col8\" class=\"data row11 col8\" >-0.44</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col9\" class=\"data row11 col9\" >-0.44</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col10\" class=\"data row11 col10\" >-0.18</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col11\" class=\"data row11 col11\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col12\" class=\"data row11 col12\" >-0.37</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow11_col13\" class=\"data row11 col13\" >0.33</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row12\" class=\"row_heading level0 row12\" >LSTAT</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col0\" class=\"data row12 col0\" >0.46</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col1\" class=\"data row12 col1\" >-0.41</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col2\" class=\"data row12 col2\" >0.6</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col3\" class=\"data row12 col3\" >-0.054</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col4\" class=\"data row12 col4\" >0.59</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col5\" class=\"data row12 col5\" >-0.61</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col6\" class=\"data row12 col6\" >0.6</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col7\" class=\"data row12 col7\" >-0.5</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col8\" class=\"data row12 col8\" >0.49</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col9\" class=\"data row12 col9\" >0.54</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col10\" class=\"data row12 col10\" >0.37</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col11\" class=\"data row12 col11\" >-0.37</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col12\" class=\"data row12 col12\" >1</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow12_col13\" class=\"data row12 col13\" >-0.74</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_598b4786_d078_11e8_b518_1831bfbb88dalevel0_row13\" class=\"row_heading level0 row13\" >MV</th> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col0\" class=\"data row13 col0\" >-0.39</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col1\" class=\"data row13 col1\" >0.36</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col2\" class=\"data row13 col2\" >-0.48</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col3\" class=\"data row13 col3\" >0.18</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col4\" class=\"data row13 col4\" >-0.43</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col5\" class=\"data row13 col5\" >0.7</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col6\" class=\"data row13 col6\" >-0.38</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col7\" class=\"data row13 col7\" >0.25</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col8\" class=\"data row13 col8\" >-0.38</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col9\" class=\"data row13 col9\" >-0.47</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col10\" class=\"data row13 col10\" >-0.51</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col11\" class=\"data row13 col11\" >0.33</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col12\" class=\"data row13 col12\" >-0.74</td> \n",
       "        <td id=\"T_598b4786_d078_11e8_b518_1831bfbb88darow13_col13\" class=\"data row13 col13\" >1</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1b0a6be7898>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ploting correaltion matrix\n",
    "corr = boston_data.corr()\n",
    "corr.style.background_gradient().set_precision(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Between prices and Lower Status Population\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.regplot(y=\"MV\",x=\"LSTAT\", data=boston_data, fit_reg= True)\n",
    "plt.title(\"Relationship between Lower Status Population and Price\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can LSTAT has a strong negative correlation with price ,as % of population with lower status increases ,the price of\n",
    "the house is decreasing ,which makes sense and the correlation is -0.73 which we computed earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Between prices and average number of rooms per dwelling\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.regplot(y=\"MV\",x=\"RM\", data=boston_data, fit_reg= True)\n",
    "plt.title(\"Relationship between average number of rooms per dwelling and Price\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Strong positive coorelation, as the number of rooms increases, the housing price increases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the Distribution of each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFjZJREFUeJzt3X2QZXV95/H3R56fFBEZcUAHhBApKUVHJBpTLWgFlYhmIWBQwUVnd0tXXbEUrd0Va+MW7kYxUik3RFQ0REB8gBU1QaCjsRSRB0VEw6ijDBAR5MEBFUe/+8c9vbk985vpOz19+/bc+35VTfU5v/O753z7xxk+cx7uOakqJEna0CNGXYAkaWkyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRAaGwl+WiSv+imn5vk+wu47i8kOaWbPjXJPy/guk9O8o8LtT5pvrYfdQHSYqiqrwCHzNUvyZnAQVX1ijnW98KFqCvJCuBHwA5Vtb5b9wXABQuxfmlreAQhbYH0+PdGE8EdXWMjyeFJrk/yiyQXATv3LZtKsrZv/m1Jbu/6fj/J0UmOAd4BnJhkXZJvdX2nk7w7yVeBh4ADu7bXzN58zklyf5LvJTm6b8GaJM/vmz8zyd91s1/uft7XbfMPNjxlleTZSa7t1n1tkmf3LZtO8j+SfLX7Xf4xyd4LMZ6SAaGxkGRH4LPAx4G9gE8C/24TfQ8BXg88s6r2AP4YWFNVXwT+J3BRVe1eVU/t+9grgVXAHsCPG6t9FvBDYG/gncCnk+w1QOl/1P3cs9vm1zaodS/gcuADwGOA9wGXJ3lMX7c/B14N7APsCLxlgO1KczIgNC6OBHYA3l9Vv6mqS4BrN9H3t8BOwKFJdqiqNVX1gznW/9Gqurmq1lfVbxrL7+rb9kXA94EXz/N36fdi4Naq+ni37U8A3wP+pK/PR6rqX6rql8DFwNMWYLuSAaGx8Xjg9pr99MnWv/SpqtXAm4AzgbuSXJjk8XOs/7Y5lre2Pdc6B/F4Nv49fgws75v/177ph4DdF2C7kgGhsXEnsDxJ+tqesKnOVfX3VfWHwBOBAt4zs2hTH5lj+61t39FNPwjs2rfscVuw3ju6Gvs9Abh9js9JW82A0Lj4GrAeeEOS7ZP8KXBEq2OSQ5IclWQn4FfAL+mddgL4KbBiHncq7dNte4ckJwBPBj7fLbsROKlbthI4vu9zPwN+Bxy4ifV+Hvi9JH/e/V4nAocCn9vC+qQtZkBoLFTVw8CfAqcC9wInAp/eRPedgLOAu+mdntmH3t1L0Lu4DXBPkuu3oIRrgIO7db4bOL6q7umW/TfgSV1d7wL+vq/uh7r+X01yX5IjN/i97gGOBU4H7gHeChxbVXdvQW3SvMQXBkmSWjyCkCQ1GRCSpCYDQpLUZEBIkpq26ae57r333rVixYp5ffbBBx9kt912W9iCtnGOyWyOx8Yck9m21fG47rrr7q6qx87Vb5sOiBUrVvDNb35zXp+dnp5mampqYQvaxjkmszkeG3NMZttWxyNJ8ykDG/IUkySpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqWmb/ib11rjp9vs59YzLR7LtNWctxLvsJWm4PIKQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoaekAk2S7JDUk+180fkOSaJLcmuSjJjl37Tt386m75imHXJknatMU4gngjcEvf/HuAs6vqYOBe4LSu/TTg3qo6CDi76ydJGpGhBkSS/YAXAx/q5gMcBVzSdTkfeGk3fVw3T7f86K6/JGkEhv1GufcDbwX26OYfA9xXVeu7+bXA8m56OXAbQFWtT3J/1//u/hUmWQWsAli2bBnT09PzKmzZLnD6Yevn7jgE86152NatW7dkaxsFx2Njjsls4z4eQwuIJMcCd1XVdUmmZpobXWuAZf/WUHUucC7AypUra2pqasMuAznngkt5702jeePqmpOnRrLduUxPTzPf8RxHjsfGHJPZxn08hvl/yOcAL0nyImBn4JH0jij2TLJ9dxSxH3BH138tsD+wNsn2wKOAnw+xPknSZgztGkRVvb2q9quqFcBJwFVVdTJwNXB81+0U4NJu+rJunm75VVW10RGEJGlxjOJ7EG8D3pxkNb1rDOd17ecBj+na3wycMYLaJEmdRTkJX1XTwHQ3/UPgiEafXwEnLEY9kqS5+U1qSVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqSmoQVEkp2TfCPJt5LcnORdXfsBSa5JcmuSi5Ls2LXv1M2v7pavGFZtkqS5DfMI4tfAUVX1VOBpwDFJjgTeA5xdVQcD9wKndf1PA+6tqoOAs7t+kqQRGVpAVM+6bnaH7k8BRwGXdO3nAy/tpo/r5umWH50kw6pPkrR5qarhrTzZDrgOOAj4a+B/A1/vjhJIsj/whap6SpLvAMdU1dpu2Q+AZ1XV3RuscxWwCmDZsmXPuPDCC+dV210/v5+f/nJ+v9fWOmz5o0az4TmsW7eO3XfffdRlLBmOx8Yck9m21fF43vOed11VrZyr3/bDLKKqfgs8LcmewGeAJ7e6dT9bRwsbpVdVnQucC7By5cqampqaV23nXHAp771pqL/+Jq05eWok253L9PQ08x3PceR4bMwxmW3cx2NR7mKqqvuAaeBIYM8kM/9n3g+4o5teC+wP0C1/FPDzxahPkrSxYd7F9NjuyIEkuwDPB24BrgaO77qdAlzaTV/WzdMtv6qGef5LkrRZwzzHsi9wfncd4hHAxVX1uSTfBS5M8hfADcB5Xf/zgI8nWU3vyOGkIdYmSZrD0AKiqr4NHN5o/yFwRKP9V8AJw6pHkrRlBjrFlOTYJH7rWpImyKD/0z8JuDXJ/0rSuhNJkjRmBgqIqnoFvdNFPwA+kuRrSVYl2WOo1UmSRmbg00ZV9QDwKeBCehegXwZcn+Q/D6k2SdIIDXoN4k+SfAa4it4jM46oqhcCTwXeMsT6JEkjMuhdTCfQe8Del/sbq+qhJP9+4cuSJI3aoNcgXgX8OMnzoffFt5nrD1V15RDrkySNyKCnmF5L7wmrf9M17Qd8dlhFSZJGb9CL1K8DngM8AFBVtwL7DKsoSdLoDRoQv66qh2dmuofp+ZwkSRpjgwbEPyV5B7BLkhcAnwT+7/DKkiSN2qABcQbwM+Am4D8Anwf+67CKkiSN3qC3ue4CfLiq/hb+/5vidgEeGlZhkqTRGvQI4kp6gTBjF+BLC1+OJGmpGDQgdq6qdTMz3fSuwylJkrQUDBoQDyZ5+sxMkmcAvxxOSZKkpWDQaxBvAj6ZZOb90fsCJw6nJEnSUjBQQFTVtUl+HzgECPC9qvrNUCuTJI3Ulrxy9JnAiu4zhyehqj42lKokSSM3UEAk+TjwJOBG4LddcwEGhCSNqUGPIFYCh1aVj9eQpAkx6F1M3wEeN8xCJElLy6BHEHsD303yDeDXM41V9ZKhVCVJGrlBA+LMYRYhSVp6Br3N9Z+SPBE4uKq+lGRXYLvhliZJGqX5vlFuOb5RTpLGmm+UkyQ1+UY5SVKTb5STJDX5RjlJUtOgdzH9Dvjb7o8kaQIM+iymH9G45lBVBy54RZKkJWFLnsU0Y2fgBGCvhS9HkrRUDHQNoqru6ftze1W9HzhqyLVJkkZo0FNMT++bfQS9I4o9hlKRJGlJGPQU03v7ptcDa4A/W/BqJElLxqB3MT1v2IVIkpaWQU8xvXlzy6vqfQtTjiRpqRj0i3Irgf9E7yF9y4H/CBxK7zpE81pEkv2TXJ3kliQ3J3lj175XkiuS3Nr9fHTXniQfSLI6ybc3uO4hSVpkW/LCoKdX1S8AkpwJfLKqXrOZz6wHTq+q65PsAVyX5ArgVODKqjoryRn0vqX9NuCFwMHdn2cBH+x+SpJGYNAjiCcAD/fNPwys2NwHqurOqrq+m/4FcAu9o4/jgPO7bucDL+2mjwM+Vj1fB/ZMsu+A9UmSFtigRxAfB76R5DP0vlH9MuBjg24kyQrgcOAaYFlV3Qm9EEky89jw5cBtfR9b27XducG6VgGrAJYtW8b09PSgZcyybBc4/bD18/rs1ppvzcO2bt26JVvbKDgeG3NMZhv38Rj0LqZ3J/kC8Nyu6dVVdcMgn02yO/Ap4E1V9UCSTXZtbbpRy7nAuQArV66sqampQcrYyDkXXMp7bxo0HxfWmpOnRrLduUxPTzPf8RxHjsfGHJPZxn08Bj3FBLAr8EBV/RWwNskBc30gyQ70wuGCqvp01/zTmVNH3c+7uva1wP59H98PuGML6pMkLaBBXzn6TnoXkt/eNe0A/N0cnwlwHnDLBrfBXgac0k2fAlza1/6q7m6mI4H7Z05FSZIW36DnWF5G7xrCzEXnO7o7kzbnOcArgZuS3Ni1vQM4C7g4yWnAT+g9+A9675h4EbAaeAh49aC/hCRp4Q0aEA9XVSUpgCS7zfWBqvpn2tcVAI5u9C96776WJC0Bg16DuDjJ39C79fS1wJfw5UGSNNYGvYvpL7t3UT8AHAL896q6YqiVSZJGas6ASLId8A9V9XzAUJCkCTHnKaaq+i3wUJJHLUI9kqQlYtCL1L+idzfSFcCDM41V9YahVCVJGrlBA+Ly7o8kaUJsNiCSPKGqflJV52+unyRp/Mx1DeKzMxNJPjXkWiRJS8hcAdH/RbcDh1mIJGlpmSsgahPTkqQxN9dF6qcmeYDekcQu3TTdfFXVI4danSRpZDYbEFW13WIVIklaWrbkfRCSpAliQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmoYWEEk+nOSuJN/pa9sryRVJbu1+PrprT5IPJFmd5NtJnj6suiRJgxnmEcRHgWM2aDsDuLKqDgau7OYBXggc3P1ZBXxwiHVJkgYwtICoqi8DP9+g+Tjg/G76fOClfe0fq56vA3sm2XdYtUmS5rbY1yCWVdWdAN3Pfbr25cBtff3Wdm2SpBHZftQFdNJoq2bHZBW901AsW7aM6enpeW1w2S5w+mHr5/XZrTXfmodt3bp1S7a2UXA8NuaYzDbu47HYAfHTJPtW1Z3dKaS7uva1wP59/fYD7mitoKrOBc4FWLlyZU1NTc2rkHMuuJT33jSafFxz8tRItjuX6elp5jue48jx2JhjMtu4j8din2K6DDilmz4FuLSv/VXd3UxHAvfPnIqSJI3G0P4JneQTwBSwd5K1wDuBs4CLk5wG/AQ4oev+eeBFwGrgIeDVw6pLkjSYoQVEVb18E4uObvQt4HXDqkWStOX8JrUkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmrYfdQGTaMUZl49ku2vOevFItitp2+QRhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpp8musEmespsqcftp5Th/SkWZ8kK217PIKQJDUZEJKkJgNCktS0pAIiyTFJvp9kdZIzRl2PJE2yJXOROsl2wF8DLwDWAtcmuayqvjvayrQt25rXu27NRXsvymscLJmAAI4AVlfVDwGSXAgcBxgQY2BU7+EelVH+vqMKp0n7bwzDvfNvLovx3zlVNfSNDCLJ8cAxVfWabv6VwLOq6vUb9FsFrOpmDwG+P89N7g3cPc/PjivHZDbHY2OOyWzb6ng8saoeO1enpXQEkUbbRulVVecC5271xpJvVtXKrV3POHFMZnM8NuaYzDbu47GULlKvBfbvm98PuGNEtUjSxFtKAXEtcHCSA5LsCJwEXDbimiRpYi2ZU0xVtT7J64F/ALYDPlxVNw9xk1t9mmoMOSazOR4bc0xmG+vxWDIXqSVJS8tSOsUkSVpCDAhJUtNEBsSkP9Ijyf5Jrk5yS5Kbk7yxa98ryRVJbu1+PnrUtS6mJNsluSHJ57r5A5Jc043HRd3NExMjyZ5JLknyvW5f+YNJ3keS/Jfu78t3knwiyc7jvo9MXED0PdLjhcChwMuTHDraqhbdeuD0qnoycCTwum4MzgCurKqDgSu7+UnyRuCWvvn3AGd343EvcNpIqhqdvwK+WFW/DzyV3thM5D6SZDnwBmBlVT2F3o00JzHm+8jEBQR9j/SoqoeBmUd6TIyqurOqru+mf0HvL/5yeuNwftftfOClo6lw8SXZD3gx8KFuPsBRwCVdl0kbj0cCfwScB1BVD1fVfUzwPkLvrs9dkmwP7ArcyZjvI5MYEMuB2/rm13ZtEynJCuBw4BpgWVXdCb0QAfYZXWWL7v3AW4HfdfOPAe6rqvXd/KTtJwcCPwM+0p12+1CS3ZjQfaSqbgf+EvgJvWC4H7iOMd9HJjEgBnqkxyRIsjvwKeBNVfXAqOsZlSTHAndV1XX9zY2uk7SfbA88HfhgVR0OPMiEnE5q6a61HAccADwe2I3eaeoNjdU+MokB4SM9gCQ70AuHC6rq013zT5Ps2y3fF7hrVPUtsucAL0myht4px6PoHVHs2Z1OgMnbT9YCa6vqmm7+EnqBMan7yPOBH1XVz6rqN8CngWcz5vvIJAbExD/Sozu/fh5wS1W9r2/RZcAp3fQpwKWLXdsoVNXbq2q/qlpBb3+4qqpOBq4Gju+6Tcx4AFTVvwK3JTmkazqa3qP3J3IfoXdq6cgku3Z/f2bGY6z3kYn8JnWSF9H7F+LMIz3ePeKSFlWSPwS+AtzEv51zfwe96xAXA0+g9xfihKr6+UiKHJEkU8BbqurYJAfSO6LYC7gBeEVV/XqU9S2mJE+jd9F+R+CHwKvp/aNyIveRJO8CTqR3F+ANwGvoXXMY231kIgNCkjS3STzFJEkagAEhSWoyICRJTQaEJKnJgJAkNRkQ0oCSPC7JhUl+kOS7ST6f5PeS/DLJjV3bx7ovIZJkqu/JsKcmqSRH963vZV3b8ZvapjRKBoQ0gO7LUZ8BpqvqSVV1KL3vjiwDflBVTwMOo/dt2j/bxGpuAl7eN38S8K3hVS1tHQNCGszzgN9U1f+ZaaiqG+l78GNV/Rb4Bpt+YNtXgCOS7NA9B+sg4MbhlSxtHQNCGsxT6D29c5OS7Aw8C/jiJroU8CXgj+k9+G2iHvGibY8BIW29JyW5EbgH+ElVfXszfS+kd2rpJOATi1GcNF8GhDSYm4FnbGLZzDWIg+g90O0lm1pJVX2D3tHI3lX1LwtfprRwDAhpMFcBOyV57UxDkmcCT5yZ716gcwbw9jnW9XZ6F7ilJc2AkAZQvadavgx4QXeb683AmWz8/P/PArsmee5m1vWFqrp6aMVKC8SnuUqSmjyCkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTf8PXxxwWUnSiWkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hPIup3w5UzhtMaHw4ZmY2VtR0FVNEvFj1eS4ivgEcWnBsZmZWolqHmKofnPceKkcUuxQSkZmZjQm1DjFdVDW9GVgN/EXDozEzszGj1quYuosOxMzMxpZah5j+x5aWD7hT2szMmsBwrmL6MJXHYQB8isrTWZ8tIigzMytfrQliInBQ/+O9JZ0P/CAiPl9UYGZmVq5aH9b3PuCtqvm3gI6GR2NmZmNGrUcQ1wD3SbqJyh3VxwBXFxaVmZmVrtarmOZJ+hHwR6no1Ih4qLiwzMysbLUOMQHsBLwSEd8E1kjaq6CYzMxsDKgpQUg6DzgbOCcVjQO+V1RQZmZWvlqPII4B/hR4DSAi1uJHbZiZNbVaE8Rb6YU+ASBp5+JCMjOzsaDWBLFI0reB8ZK+ANyOXx5kZtbUar2K6WvpXdSvAPsA/xARtxUamZmZlWrIIwhJ20i6PSJui4i/i4gv15IcJF0haYOkR6vKzpf0nKSH0+fIqmXnSFol6QlJn6i/S2Zm1ghDJoiIeBt4XdKuw9z2VcDMTPnFETE9fW4FkLQvcALwB2mdf5W0zTDbMzOzBqr1TupfA8sl3Ua6kgkgIr442AoRcaekjhq3PwvoiYg3gaclrQIOAe6ucX0zM2uwWhPELenTCGdIOhl4ADgrIjYCk4F7quqsSWXvImkOMAegvb2d3t7euoJo3xHO2n9zXeuOVL0xj1RfX19pbZfFfW4N7nMxtpggJL0vIp6JiAUNau8y4AIql8teQOVNdZ8DlKkbuQ1ExHxgPkBnZ2d0dXXVFcilCxdz0fJa82NjrT6xq5R2e3t7qffntbVyn1uD+1yMoc5B/LB/QtINI20sItZHxNsR8Q6Vy2QPSYvWAFOrqk4B1o60PTMzq99QCaL6L/u9R9qYpElVs8cA/Vc4LQFOkLR9esbTNOC+kbZnZmb1G2qMJQaZHpKka4EuYKKkNcB5QJek6Wlbq4G/BIiIFZIWAY8Bm4HT09VTZmZWkqESxAGSXqFyJLFjmibNR0T8zmArRsSnM8WXb6H+PGDeEPGYmdko2WKCiAjfi2Bm1qKG8z4IMzNrIU4QZmaW5QRhZmZZThBmZpblBGFmZllOEGZmluUEYWZmWU4QZmaW5QRhZmZZThBmZpblBGFmZllOEGZmluUEYWZmWU4QZmaW5QRhZmZZThBmZpblBGFmZllOEGZmllVYgpB0haQNkh6tKpsg6TZJT6bv3VK5JF0iaZWkRyQdVFRcZmZWmyKPIK4CZg4omwssjYhpwNI0D3AEMC195gCXFRiXmZnVoLAEERF3Ai8NKJ4FLEjTC4Cjq8qvjop7gPGSJhUVm5mZDU0RUdzGpQ7g5ojYL82/HBHjq5ZvjIjdJN0MXBgRd6XypcDZEfFAZptzqBxl0N7efnBPT09dsW14aRPr36hr1RHbf/KupbTb19dHW1tbKW2XxX1uDe7z8HR3dy+LiM6h6m1b19YbT5mybOaKiPnAfIDOzs7o6uqqq8FLFy7mouXldH/1iV2ltNvb20u9P6+tlfvcGtznYoz2VUzr+4eO0veGVL4GmFpVbwqwdpRjMzOzKqOdIJYAs9P0bGBxVfnJ6WqmGcCmiFg3yrGZmVmVwsZYJF0LdAETJa0BzgMuBBZJOg14BjguVb8VOBJYBbwOnFpUXGZmVpvCEkREfHqQRYdl6gZwelGxmJnZ8PlOajMzy3KCMDOzLCcIMzPLcoIwM7MsJwgzM8tygjAzsywnCDMzy3KCMDOzLCcIMzPLcoIwM7MsJwgzM8tygjAzsywnCDMzy3KCMDOzLCcIMzPLcoIwM7MsJwgzM8tygjAzsywnCDMzyyrsndRbImk18CrwNrA5IjolTQCuAzqA1cBfRMTGMuIzM7NyjyC6I2J6RHSm+bnA0oiYBixN82ZmVpKxNMQ0C1iQphcAR5cYi5lZy1NEjH6j0tPARiCAb0fEfEkvR8T4qjobI2K3zLpzgDkA7e3tB/f09NQVw4aXNrH+jbpWHbH9J+9aSrt9fX20tbWV0nZZ3OfW4D4PT3d397Kq0ZtBlXIOAvh4RKyVtAdwm6THa10xIuYD8wE6Ozujq6urrgAuXbiYi5aX0/3VJ3aV0u6lCxdz0V2vldL26gs/WUq7vb291PtvZGvlPreG0ehzKUNMEbE2fW8AbgIOAdZLmgSQvjeUEZuZmVWMeoKQtLOkXfqngf8GPAosAWanarOBxaMdm5mZ/ZcyxljagZsk9bf//Yj4saT7gUWSTgOeAY4rITYzM0tGPUFExFPAAZnyF4HDRjseMzPLG0uXuZqZ2RjiBGFmZllOEGZmluUEYWZmWU4QZmaW5QRhZmZZThBmZpblBGFmZllOEGZmluUEYWZmWU4QZmaWVdb7IMzMGmb5c5s4Ze4to95uWe85GS0+gjAzsywfQdio6CjhrzuAq2buXEq7Zs3ARxBmZpblI4gSlPXX9Fn7l9Jsqcoam4bmH5+25ucEYWZWp7L+2IPRGT71EJOZmWX5CMKsIK12Yr7Mv6Zbcfh0NIy5IwhJMyU9IWmVpLllx2Nm1qrG1BGEpG2AbwF/AqwB7pe0JCIeKzcys61HmSfmrbmMtSOIQ4BVEfFURLwF9ACzSo7JzKwlKSLKjuE/SToWmBkRn0/zJwEfiYgzqurMAeak2X2AJ+psbiLwwgjC3Rq5z63BfW4NI+nz+yNi96EqjakhJkCZst/KYBExH5g/4oakByKic6Tb2Zq4z63BfW4No9HnsTbEtAaYWjU/BVhbUixmZi1trCWI+4FpkvaStB1wArCk5JjMzFrSmBpiiojNks4AfgJsA1wRESsKam7Ew1RbIfe5NbjPraHwPo+pk9RmZjZ2jLUhJjMzGyOcIMzMLKslE0QrPM5D0lRJd0haKWmFpDNT+QRJt0l6Mn3vVnasjSRpG0kPSbo5ze8l6d7U3+vSxQ9NQ9J4SddLejzt64+2wD7+7+nf9KOSrpW0Q7PtZ0lXSNog6dGqsux+VcUl6ffZI5IOalQcLZcgqh7ncQSwL/BpSfuWG1UhNgNnRcSHgBnA6amfc4GlETENWJrmm8mZwMqq+a8CF6f+bgROKyWq4nwT+HFE/D5wAJW+N+0+ljQZ+CLQGRH7UbmY5QSabz9fBcwcUDbYfj0CmJY+c4DLGhVEyyUIWuRxHhGxLiIeTNOvUvnFMZlKXxekaguAo8uJsPEkTQE+CXw3zQs4FLg+VWm2/v4O8MfA5QAR8VZEvEwT7+NkW2BHSdsCOwHraLL9HBF3Ai8NKB5sv84Cro6Ke4DxkiY1Io5WTBCTgWer5teksqYlqQM4ELgXaI+IdVBJIsAe5UXWcN8A/h54J82/F3g5Ijan+Wbb13sDzwNXpmG170ramSbexxHxHPA14BkqiWETsIzm3s/9Btuvhf1Oa8UEMeTjPJqJpDbgBuBLEfFK2fEURdJRwIaIWFZdnKnaTPt6W+Ag4LKIOBB4jSYaTspJ4+6zgL2APYGdqQyxDNRM+3kohf07b8UE0TKP85A0jkpyWBgRN6bi9f2Hn+l7Q1nxNdjHgT+VtJrKsOGhVI4oxqehCGi+fb0GWBMR96b566kkjGbdxwCHA09HxPMR8RvgRuBjNPd+7jfYfi3sd1orJoiWeJxHGn+/HFgZEV+vWrQEmJ2mZwOLRzu2IkTEORExJSI6qOzTn0bEicAdwLGpWtP0FyAifgU8K2mfVHQY8BhNuo+TZ4AZknZK/8b7+9y0+7nKYPt1CXByupppBrCpfyhqpFryTmpJR1L567L/cR7zSg6p4ST9IfAfwHL+a0z+XCrnIRYB76Pyn+24iBh4MmyrJqkL+HJEHCVpbypHFBOAh4DPRsSbZcbXSJKmUzkpvx3wFHAqlT/8mnYfS/oKcDyVK/UeAj5PZcy9afazpGuBLiqP9F4PnAf8kMx+TYnyX6hc9fQ6cGpEPNCQOFoxQZiZ2dBacYjJzMxq4ARhZmZZThBmZpblBGFmZllOEGZmluUEYTYCko6R9PCAzzuS/lpSSPrbqrr/IumUEsM1GxYnCLMRiIibImJ6/wf4Vyr3n/yEyp2uZ27tj5621uUEYdYgkj4I/ANwEpWbE5+n8ljm2Vtaz2yscoIwa4D03KvvU7mD+5mqRRcCZ6X3kJhtVZwgzBrjAmBFRPRUF0bE08B9wGdKicpsBLYduoqZbUl69tOfU3mSas4/U3nS6p2jFZNZI/gIwmwE0vsJrgROTm/ue5eIeJzKE0ePGs3YzEbKRxBmI/NXVN7sdVnloZr/6doB9eZRecqo2VbDT3M1M7MsDzGZmVmWE4SZmWU5QZiZWZYThJmZZTlBmJlZlhOEmZllOUGYmVnW/wdUyMHpePdAYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the distribution of each variable\n",
    "cols = list(boston_data.columns)\n",
    "for c in cols:\n",
    "    plt.hist((boston_data[c]))\n",
    "    plt.title(\"distribution \")\n",
    "    plt.xlabel(c)\n",
    "    plt.ylabel(\"Frequencey\")\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* CRIM has exponential decay,so we we will do logarthimic transforamtion\n",
    "* Most of the neighborhoods donot tract Charles River \n",
    "* Age feature has a left skewed distibution\n",
    "* weighted distances to five Boston employment centres (DIS) has right skewed distribution\n",
    "* LSTAT has a right-skewed distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#doing feature transformations\n",
    "bos = boston_data\n",
    "bos['CRIM'] = np.log(bos['CRIM'])\n",
    "bos['LSTAT'] = np.log(bos['LSTAT'])\n",
    "bos['AGE'] = np.square(bos['AGE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting the train and test data\n",
    "Y = (bos[\"MV\"])\n",
    "X_data = bos.drop(\"MV\",axis =1)\n",
    "\n",
    "#standardizing the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#standardizing the columns\n",
    "std_data = MinMaxScaler().fit_transform(X_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train - Predictors shape (354, 13)\n",
      "Test - Predictors shape (152, 13)\n",
      "Train - Target shape (354,)\n",
      "Test - Target shape (152,)\n"
     ]
    }
   ],
   "source": [
    "#spliting data\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train ,x_test ,y_train,y_test = train_test_split(std_data,Y,test_size = 0.3 ,random_state =124 )\n",
    "print('Train - Predictors shape', x_train.shape)\n",
    "print('Test - Predictors shape', x_test.shape)\n",
    "print('Train - Target shape', y_train.shape)\n",
    "print('Test - Target shape', y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear-Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initializing the linear regression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lm = LinearRegression(fit_intercept = True ,normalize=False,n_jobs = -1)\n",
    "lm.fit(x_train, y_train)\n",
    "\n",
    "y_pred = lm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error : 23.799171107999726\n",
      "Root Mean Squared Error: 4.878439413173\n"
     ]
    }
   ],
   "source": [
    "# Compute and print MSE and RMSE\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mse = mean_squared_error(y_test,y_pred)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "R_squared= lm.score(x_test,y_test)\n",
    "print(\"Mean squared error : {}\".format(mse))\n",
    "print(\"Root Mean Squared Error: {}\".format(rmse))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting \n",
    "plt.figure(figsize=(10,8))\n",
    "sns.set_style('whitegrid')\n",
    "sns.regplot(x=y_test,y=y_pred)\n",
    "plt.xlabel(\"Prices: $Y_i$\")\n",
    "plt.ylabel(\"Predicted prices: $\\hat{Y}_i$\")\n",
    "plt.title(\"Prices vs Predicted prices: $Y_i$ vs $\\hat{Y}_i$\")\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the distribution of erros\n",
    "y_error  = y_test-y_pred\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.kdeplot((y_error), bw=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients of best fit line\n",
      "[  0.10223247   2.19949396   1.15413068   2.44721631  -8.56359847\n",
      "  11.37030278   2.96274044  -9.70773942   4.92004789  -5.78589198\n",
      "  -6.98268318   4.33506555 -28.22146822]\n",
      "Intercept of the Best fit line : 36.905897280028604 \n"
     ]
    }
   ],
   "source": [
    "#getting the intercept and coefficients of the fitted linear model\n",
    "print(\"Coefficients of best fit line\")\n",
    "print(lm.coef_)\n",
    "print(\"Intercept of the Best fit line : {} \".format( lm.intercept_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>0.102232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>2.199494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>1.154131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>2.447216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>-8.563598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>11.370303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>2.962740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-9.707739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>4.920048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>-5.785892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>-6.982683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4.335066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>-28.221468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Coefficients\n",
       "CRIM       0.102232\n",
       "ZN         2.199494\n",
       "INDUS      1.154131\n",
       "CHAS       2.447216\n",
       "NOX       -8.563598\n",
       "RM        11.370303\n",
       "AGE        2.962740\n",
       "DIS       -9.707739\n",
       "RAD        4.920048\n",
       "TAX       -5.785892\n",
       "PT        -6.982683\n",
       "B          4.335066\n",
       "LSTAT    -28.221468"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the coefficients of each column\n",
    "coefficients = pd.DataFrame(lm.coef_,X_data.columns)\n",
    "coefficients.columns = ['Coefficients']\n",
    "coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression with Stochastic Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#standardizing the data\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#standardizing the columns\n",
    "std_data1 = MinMaxScaler().fit_transform(boston_data)\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train1 ,x_test1  = train_test_split(std_data1,test_size = 0.3 ,random_state =12488 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#utility function for predictions\n",
    "def pred_price(row, coefficients):\n",
    "    y_pred = coefficients[0]\n",
    "    for i in range(len(row)-1):\n",
    "        y_pred += coefficients[i + 1] * row[i]\n",
    "    return y_pred\n",
    "\n",
    "\n",
    "\n",
    "#computing optimal weights using sgd\n",
    "def coefficients_sgd(train, learning_rate, n_epoch):\n",
    "    coef = [0.0 for i in range(len(train[0]))]\n",
    "    for epoch in range(n_epoch):\n",
    "        for row in train:\n",
    "            y_pred = pred_price(row, coef)\n",
    "            error = y_pred - row[-1]\n",
    "            coef[0] = coef[0] - learning_rate * error\n",
    "            for i in range(len(row)-1):\n",
    "                coef[i + 1] = coef[i + 1] - learning_rate * error * row[i]\n",
    "\n",
    "    return coef\n",
    "\n",
    "\n",
    "#implenting sgd on linear-regression\n",
    "def linear_regression_sgd(train, test, learning_rate, n_epoch):\n",
    "    predictions = list()\n",
    "    coef = coefficients_sgd(train, learning_rate, n_epoch)\n",
    "    for row in test:\n",
    "        y_pred = pred_price(row, coef)\n",
    "        predictions.append(y_pred)\n",
    "    return(predictions)\n",
    " \n",
    "    \n",
    "coef_sgd = coefficients_sgd(x_train1,0.001,100)   \n",
    "y_pred_sgd = linear_regression_sgd(x_train1,x_test1,0.01,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error : 23.045074577917465\n",
      "Root Mean Squared Error: 4.800528572763365\n"
     ]
    }
   ],
   "source": [
    "y_test1 =[]\n",
    "for i in range(len(x_test1)):\n",
    "    price = x_test1[i][-1]\n",
    "    y_test1.append(price)\n",
    "    \n",
    "#my_list = y_test1\n",
    "y_test_final = [((i * 45)+5) for i in y_test1]\n",
    "#my_list1 = y_pred\n",
    "y_pred_final = [((i * 45)+5) for i in y_pred_sgd]\n",
    "mse_sgd = mean_squared_error(y_test_final,y_pred_final)\n",
    "rmse_sgd =mse_sgd**0.5\n",
    "print(\"Mean squared error : {}\".format(mse_sgd))\n",
    "print(\"Root Mean Squared Error: {}\".format(mse_sgd**0.5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting \n",
    "plt.figure(figsize=(10,8))\n",
    "sns.set_style('whitegrid')\n",
    "sns.regplot(x=pd.Series(y_test_final),y=pd.Series(y_pred_final))\n",
    "plt.xlabel(\"Prices: $Y_i$\")\n",
    "plt.ylabel(\"Predicted prices: $\\hat{Y}_i$\")\n",
    "plt.title(\"Prices vs Predicted prices: $Y_i$ vs $\\hat{Y}_i$\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the distribution of erros\n",
    "y_error_SGD  = pd.Series(y_test_final)-pd.Series(y_pred_final)\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.kdeplot(np.array(y_error_SGD), bw=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SGD-Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initializing SGD-regressor\n",
    "from sklearn.linear_model.stochastic_gradient import SGDRegressor\n",
    "sgd_reg = SGDRegressor(loss='squared_loss', penalty='l2', alpha=0.0001, l1_ratio=0,\n",
    "                       fit_intercept=True, max_iter=100, random_state=123, learning_rate='constant',\n",
    "                       eta0=0.01)\n",
    "sgd_reg.fit(x_train, y_train)\n",
    "\n",
    "y_pred2 = sgd_reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error : 24.796796575494508\n",
      "Root Mean Squared Error: 4.979638197248321\n"
     ]
    }
   ],
   "source": [
    "# Compute and print MSE and RMSE\n",
    "\n",
    "mse1 = mean_squared_error(y_test,y_pred2)\n",
    "rmse1 = np.sqrt(mean_squared_error(y_test, y_pred2))\n",
    "\n",
    "print(\"Mean squared error : {}\".format(mse1))\n",
    "print(\"Root Mean Squared Error: {}\".format(rmse1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting \n",
    "plt.figure(figsize=(10,8))\n",
    "sns.set_style('whitegrid')\n",
    "sns.regplot(x=y_test,y=y_pred2)\n",
    "plt.xlabel(\"Prices: $Y_i$\")\n",
    "plt.ylabel(\"Predicted prices: $\\hat{Y}_i$\")\n",
    "plt.title(\"Prices vs Predicted prices: $Y_i$ vs $\\hat{Y}_i$\")\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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peEcfESkOwxSRBKJBZX7zm+qKo83qfQvUhJ7M6tl8RZvQuc1HRMrCMEUkgRF3ANb5bvPFZk2NpL8JPRiOYNwXSmvPFBDtm+IdfUSkNAxTRCkKhCJw+kLzXvUpM+uh16gWpAl9dHLGlPQDO6dqKjfB6QthyOlP6/chIsokDFNEKRpNchimIAioKzYsyKyp0YmjZIoWYJsPAPumiEhR4oapSCSC++67D5s3b8a2bdvQ19d3yXPsdjtuvvlm+P3Rf436fD585StfwdatW/H5z38edrtd+sqJMsSIK/nJ4ousCxOm7BNHyRQvwDYfwAOPiUhZ4oapvXv3IhAIYM+ePbj33nuxc+fOaY+/8sor+OxnP4vh4eHJr/3yl79Ec3MzHnvsMXz84x/HI488In3lRBliMqgkEabqio04bfekvccotnqW7pWpEpMOhQYtDzwmIkWJG6Y6Ojqwfv16AMCaNWvQ2dk5/Q1UKuzatQuFhYUzvubaa6/F66+/LmXNRBllxB1dkZ1vAzoQXZnyBMKwudLbYxQLfMnUOB+CIKCpzITueW7z9Y958bv3+tm4TkRZKe7ZFy6XCyaTafK/1Wo1QqEQNJroS6+55poZX2M2R5f7jUYjnM6Zl/y7urqSKjrb+Xw+xX72dJPj2nb1OgAAw/19CNnnee6dO7rF98o7XVhelid1aZPePzUKABg83YsRdXLHySR6bUt0Ibza58bRo0cTngj/rRcG8E6/F0++cQJfvboUuiRrzFb8MyF9eG3Th9f2grhhymQywe2+cOt2JBKZDFKJvMbtdqOgoGDG57W0tMyn1pzR1dWl2M+ebnJc2z+ceR+CMILLV6+AWjW/EKArcQF/HIRgLkVLS02aKgSE450oyHOideXypN8j0Wt72Uge/nD8KMrqGlBiij976/BZB97p78W6RUV4sXcUrogO/75tXdq3JDMJ/0xIH17b9FHate3o6Jj1sbjbfG1tbWhvbwcAHDx4EM3NzXG/YVtbG15++WUAQHt7O9atW5dorURZZ2TizLv5BikAqCnKhyAg7eMRhl1+lJjnN1Q0WU3l87uj75GXulGQp8FP/+wy/NOWNTh4dgx3/Og1nBpeuEOgiYhSETdMbdy4ETqdDlu2bMH999+Pb37zm9i1axf++Mc/zvqaO++8EydOnMCdd96JPXv24O6775a0aKJMkspkcb1GjSpLftrv6Bt2BRJaJZJC7I6+7gTu6OsecuHZI4P406sWw5ynxcfWVOOxP78CY54Atv7kACIR9lARUeaLu82nUqmwffv2aV9raGi45Hkvvvji5K/z8/Pxz//8zxKUR5T5RtyBlEYO1BYvRJjyY1mFOa3fI6a8QA+zXpPQHX3/9nIP9BoV/uyaxZNf+8DiYnzj1mX4+v8cxqkRN+pLTbO/ARFRBuDQTqIUpXrm3aJiY/q3+Zz+BVuZEgQBjeWmuNt858a8ePLdc9hyWR2sF9XWWhO9O/jwOUfa6iQikgrDFFGK7O4Aik3Jh6k6qwHDLj/c/pCEVV0QCEXP5VuoMAUATRMHHs816uAn7b0AgL+4tn7G1+s1Khw6yzBFRJmPYYooBeGIiDHP/A85nmrywOM0bfVNzsFKIfDN1xVLrBh2BbDv/aEZHx92+fHLN0/j9rXVqCrMv+RxjVqFFVUFOMwwRURZgGGKKAUObxARMbnp5zF1xekNU8PO6MDOhVyZ+pM1VagtzscP9p6YcXXqkX09CIQj+OJ1l/ZfxrTWFKKz34Ewm9CJKMMxTBGlwD6x6pNqzxQAnE5T39TwRI0LGaa0ahW+cn0TDp114MVj01enOs858NPXTuLOy+vQMEdzeWuNBZ5AGL02Hk1DRJmNYYooBbFDjq3G5IOKxaCFJV+LPnt65ioNO2NhamGHYN7eVo26YsO01alQOIJv/vowrCY9vv6hZXO+vrXGAgB4j1t9RJThGKaIUpDKIcdTLbIa0nZH37Br4bf5gInVqRsacficA3u7oqtT//V6Hw6fc+A7ty2HJV875+uXlJhg1Klx+OzYQpRLRJQ0himiFIxIFKZqiw04k64GdJcf+Vo1jPq4Y+Ukd/vaaiyyGvCDvcdxdtSDh55/H9cvLcVHVlXGfa1aJWBFtQWHOB6BiDIcwxRRCmIrU0XGuVdZ4llUbMDZUS9C4YgUZU0z7PIv6J18U2nUKnzlhiYc6R/Hp//jDYgisP1jKxM+ALm12oKj/eMIpuG6EBFJhWGKKAV2dwBmvQZ6jTql91lsNSIUEXFuzCtRZReMuBfuKJmZfHxNFRZPbGPes7EJtRN3LyaitbYQ/lAk4XP+iIjkwDBFlIJUB3bGLCmN3tHXm4bDfW0LOP18Jhq1CjvuWIW7rqzDZ69ZMq/XtlZHm9APn2PfFBFlLoYpohSkepRMTH1JNEydtEkfpqKHHMuzzRdzdUMJ/u/HV0Gjnt8fOYusBpjzNJyETkQZjWGKKAUj7tSmn8cUG3UoyNOgd1ja7axIRITdLe/KVCoEQUBrjYVhiogyGsMUUQrsbr8kK1OCIKC+1ISTEm/zjXoCiIgLP2NKSquqC3FscBz+UFjuUoiIZsQwRZQkURRhdwdQJEGYAqJbfb0Sb/PFZkxZs3RlCogO7wyGRbw/6JS7FCKiGTFMESXJ6Q8hGBYl2eYDgPpSIwYcPngCIUneD4jOmAIWfmCnlFZNNKFzq4+IMhXDFFGS7K7YwE5pgsqSkug5dVJu9dlc8hwlI6WaonwUG3U4zDBFRBmKYYooSXZP7Fw+6VamAGnDlFxHyUhJEASs4iR0IspgDFNESbqwMiVNmFpslX48wojLD41KiHsOXqZbUVWAE+ednIRORBmJYYooSVIdchyTr1OjypIn6eDOYVf0bkOVKrHjWzLVkpLohPj+NEyIJyJKFcMUUZJihxxLee5dfalJ4jAl71EyUlk8MdT01Eh6DoMmIkoFwxRRkuxuP/K0Khh0Gsnec0mJEb02F0RRlOT9Rlx+lJizP0wtskbP8+sbkX5CPBFRqhimiJI04g6g2CDtXXL1pUY4faHJVa9UDbsCKJFoG1JOpSY9DDo1Tg1zZYqIMg/DFFGSpDrkeKolE9tZUgzvFEURthxZmRIEAYusRq5MEVFGYpgiStKoOyDZjKmYhtLYrKnUz+hz+UMIhCJZPWNqqsVWA04xTBFRBmKYIkqSVIccT1VVmA+dRiXJytTkUTISBz651FkNOGP3IhyRpp+MiEgqDFNESbK7A5KNRYhRqwQsthokuaNvODb9PAe2+YDoHK5AOIIBB8cjEFFmYZgiSoIvGIYnEJY8TAHRvikppqCP5MBRMlNduKOPTehElFkYpoiSMDljKg1hqr7UhL4RN0IpTvu25cBRMlPFJsSzb4qIMg3DFFESYkfJFKVpZSoYFnEuxWnfw87oylQ6Vs/kUFGQB51GxZUpIso4DFNESRhxR4NKWlamJBqPMOL2o8ighVadG7/NVSoBi4oNOCXhhHgiIinkxp+yRAts1CPtuXxT1U+MR0i1CX3YGYA1R7b4YqKzprgyRUSZhWGKKAl2dxBAesJUkUELS74WvbbUZk0Nu/w503wes9hqQJ/djQjHIxBRBmGYIkqCwxsNU+Y8reTvLQgC6ktTv6NvxJ0bhxxPtajECF8wgqGJfjAiokzAMEWUhHFvEAV5GqhVQlreX4rxCMNOf86FqcUT4xF4Rx8RZRKGKaIkjHkCsBikX5WKaSg1YcDhg8sfSur1vmAYTn8oB7f5os35PKOPiDIJwxRREhzeIArz0xdUllWYAQDHBsaTen1sDlaurUxVWvKgVQs4xSZ0IsogDFNESRjzBmHJT9/K1KpqCwDg8DlHUq+PzZjKtbv5NGoVaosMXJkioozCMEWUBIc3mNZtvrKCPJSa9eg8l9zK1HCOHSUz1SKrAaeGuTJFRJmDYYooCQ5PelemAGBlVQGO9Ce3MjWSY0fJTLXIasRpuweiyPEIRJQZGKaI5kkUxejKVJrD1KpqC04MueALhuf92sFxH4DcDFOLrQa4/KHJvjAiIrkxTBHNkycQRigiojDNYWpFtQXhiIiuJJrQe2wuVBfmI1+nTkNl8lpUwjv6iCizMEwRzdPYxMDOtG/zTTShd/bPP0x1D7nQUGaSuqSMEBuPwL4pIsoUDFNE8+TwRMNUYRob0AGgypKHYqMOnWfn1zcViYjosbnQWJqbYaq6MB9qlcCVKSLKGAxTRPM05o326hSkeWVKEASsqCpA5zyb0M+NeeELRtBUnpthSqdRobown7OmiChjMEwRzdP4xDZfOod2xqystuD4eSf8ocSb0LuHogckN+boNh8QHY/AlSkiyhQMU0TzFDvkOJ1zpmJWVVsQDIs4PuhK+DWTYSpHt/mAaJg6OezmeAQiyggMU0TzNOZZmAZ0AFhZFWtCT3yr78SQEyUmHYqMuTewM2ZJiQnjPo5HIKLMwDBFNE8ObxAalQDjAowdqC3OR0GeZl7HynQPudCQw6tSANBQGr2jr9fGrT4ikh/DFNE8xc7lEwQh7d9LEASsrLbgSIJhShRFdA+5crpfCsBkWOy1Jb79SUSULgxTRPOU7nP5Lray2oKuQSeC4Ujc59pcfoz7QjkfpqoK86HTqNDDMEVEGYBhimieFuJcvqlWVBUgEIrgxPn4waF74jlNZeZ0lyUrtUpAfYmR23xElBEYpojmaSHO5ZtqVXXiTejdttwfixBTX2pE7zDDFBHJj2GKaJ4c3mDaz+WbarHVCJNek1DfVPeQCya9BuUFuXfA8cXqS0w4bfcgEIq//UlElE4MU0TzNOYJLOjKlEolYHlVQUJ39J04H20+X4jmeLk1lBkRjog4befqFBHJi2GKaB7CERFOfwgWw8LOcFpZZcHRgXGE4jShd9ty/06+mPqS6OfsYd8UEcksbpiKRCK47777sHnzZmzbtg19fX3THn/iiSdwxx13YNOmTdi3bx8AoL+/H3fddRc+/elP40tf+hK8Xm96qidaYE5fEKK4MAM7p7p8STF8wQjeOGmf9TkObxA2p185YWpi1hTv6CMiucUNU3v37kUgEMCePXtw7733YufOnZOP2Ww27N69G48//jgeffRRPPzwwwgEAvjpT3+KW2+9Fb/4xS/Q1NSEX/3qV2n9EEQLxTF5Lt/ChqnrlpbCoFPj6cMDsz4ndoxMk0LClDlPizKznnf0EZHsNPGe0NHRgfXr1wMA1qxZg87OzsnHDh06hLVr10Kn00Gn06Gurg7Hjh1DS0sLBgcHAQAulwsVFRUzvndXV5cUnyHr+Hw+xX72dEv3tT0+7AcAjI8MoqvLmbbvM5PLqvLw1MGz2Nqshlp1aU/UKyfGo78YP4+urtlXsJKViT+3FUYBR07bMq6u+crEa5sreG3Th9f2grhhyuVywWS68C9dtVqNUCgEjUYDl8sFs/nCPBuj0TgZnh566CE89dRTCAQCuPvuu2d875aWFgk+Qvbp6upS7GdPt3RfW9txG4BzWNlcj5bFxWn7PjO5M1yIl3/+Dhz6MlzTWHLJ47/uPQqdxo7rLls1Y9hKVSb+3K48FsLThwawbNmyrG66z8Rrmyt4bdNHade2o6Nj1sfibvOZTCa43ReW0SORCDQazYyPud1umM1mPPjgg7j//vvx9NNP41vf+ha+/vWvp1I/UcaQa5sPAK5bWgaDTo2nDs281Rc7ky8dQSpTNZSa4PAGYeeBx0Qko7hhqq2tDe3t7QCAgwcPorm5efKx1tZWdHR0wO/3w+l0oqenB83NzSgoKJhcsSorK8P4+HiayidaWGMTYWqhG9ABIE+rxo0t5XjuyOCMd/WdUMCZfBeLNaFzeCcRySnuNt/GjRuxf/9+bNmyBaIoYseOHdi1axfq6upw4403Ytu2bdi6dStEUcQ999wDvV6Pv/u7v8P27dsRiUQgiiLuu+++hfgsRGk3PhGmCmQIUwDwkVWV+P17/TjQa8cHmy5s9XkDYZwb8+JT62plqUsuDbHxCEMuXLbA265ERDFxw5RKpcL27dunfa2hoWHy15s2bcKmTZumPd7Y2Iif/exnEpVIlDnGPAHkaVXI06pl+f7XLS2FceKuvqlhqsfmgigq4xiZqaqLogcec2WKiOTEoZ1E8xA9SmZhB3ZOFdt6UM0EAAAgAElEQVTqe7ZzYNpW39H+6FZ6U7mywpRaJWCJ1YhezpoiIhkxTBHNw0IfcjyTD6+qxKgniNd7RxCOiPjRSz341pOHUVOUj8VWo6y1yaG+1Mgp6EQkq7jbfER0wZhH/jAV2+r7r9dO4Qd7T6CjbxS3rqzA929fBZ1Gef8+aig14fmj5xEIRRT5+YlIfgxTRPPg8AZRW2yQtYbYVt/v3uuHOU+DH2xeg4+tqcrqOUupqC+9cOBxY5k5/guIiCTGMEU0Dw5vECtlXpkCgC9d3wBzngZfvr4RVYX5cpcjq/rSCwceM0wRkRwYpojmIdqALn+YWlZRgO/fvkruMjLC5Kwp9k0RkUzYYECUoEAoAk8gLHvPFE1XkKdFqVmPHt7RR0QyYZgiSlDsKBmLgWEq09SXcDwCEcmHYYooQQ4Zj5KhuTWUmdA95IIoinKXQkQKxDBFlCCHN3qYLsNU5llVbcG4L4RTIx65SyEiBWKYIkpQbGWq0CDfBHSa2bpFRQCAjr5RmSshIiVimCJK0JiH23yZqrHUBHOehmGKiGTBMEWUoMmVKYapjKNSCWirK8I7DFNEJAOGKaIExcJUAcNURmqrK8LxISfGfUG5SyEihWGYIkrQmCcIs14DtUqZx7ZkunWLiiCKwMHTY3KXQkQKwzBFlKBxb5AzpjLY6loLVAKb0Ilo4TFMESVozBtk83kGM+dpsbSiAO+cZpgiooXFMEWUIIc3iEKuTGW0dYsK8e7pMYQjHN5JRAuHYYooQWOeAFemMty6RUVw+UM4ft4pdylEpCAMU0QJcnhDDFMZbl1dMQBwq4+IFhTDFFECRFGMNqDnc/p5JqstzkeJSccmdCJaUAxTRAnwBsMIhCNcmcpwgsDhnUS08BimiBJw4Vw+hqlMt25REU6NeDDs8stdChEpBMMUUQJ4Ll/2iB16zNUpIlooDFNECeC5fNljZbUFWrWADjahE9ECYZgiSgDP5cseeVo1VlZb8G4fj5UhooXBMEWUAAe3+bLKZYuLcfDMGJw89JiIFgDDFFECxrwBAECRkaMRssEtKyoQCEfwwtHzcpdCRArAMEWUgFFPEBqVAKNOLXcplIC2ukJUF+bjqUMDcpdCRArAMEWUgDFPAIUGHQRBkLsUSoAgCPhoayVeOWHDmCcgdzlElOMYpogSMOoOoogzprLKR1urEAyLeO7IoNylEFGOY5giSsCoJ4AiA/ulssnK6gIsshq41UdEaccwRZQAhzfI6edZRhAE3NZahdd6RjgNnYjSimGKKAGjngDDVBb66OpKhCMi/tDJrT4iSh+GKaI4RFHEqCfIbb4stLTcjMYyE556r1/uUogohzFMEcXhDYYRCEVQyDCVdWJbfW+esuP8uE/ucogoRzFMEcURO+SYd/Nlp4+uroQoAs8cZiM6EaUHwxRRHKMTc4rYM5WdGkpNaKkswO+51UdEacIwRRRHbGWK23zZ66OtlXjn9BgGHdzqIyLpMUwRxRFbmWIDeva6ZUUFAOD5o7yrj4ikxzBFFMcoe6ayXmOZCQ2lRk5DJ6K0YJgiisMxsTJlYZjKaresqMCBXjvP6iMiyTFMEcUx6gnCoFNDr1HLXQql4JYVFQhHROztGpK7FCLKMQxTRHHwXL7c0FpjQaUlj1t9RCQ5himiOMY8PJcvFwiCgFtWVKD9uA2eQEjucogohzBMEcXBlanccfOKcvhDEbQft8ldChHlEIYpojgcniCbz3PE5YuLUWTQ4lkefExEEmKYIoojujLFMJULNGoVbmopxx+PDSEQishdDhHlCIYpojlEIiIc3iC3+XLILSsq4PSFcKB3RO5SiChHMEwRzWHcF0RE5FEyueSDTSUw6NR4lnf1EZFEGKaI5jDG6ec5J0+rxnVLS7H36HmIoih3OUSUAximiOYQO5ePoxFyy9UNJRhy+nHG7pW7FCLKAQxTRHOIrUxxmy+3tNUVAQDeOT0qcyVElAsYpojmEFuZYgN6bllaYYZRp2aYIiJJMEwRzWGUPVM5Sa0SsKauEB19DFNElLq4YSoSieC+++7D5s2bsW3bNvT19U17/IknnsAdd9yBTZs2Yd++fQAAj8eDr33ta9i6dSs+9alP4dChQ+mpnijNHJ4ABAEw5zFM5Zq2uiIcG3TyaBkiSlncMLV3714EAgHs2bMH9957L3bu3Dn5mM1mw+7du/H444/j0UcfxcMPP4xAIIBHH30UTU1NeOyxx/C9730Pvb29af0QROky6gnCkq+FWiXIXQpJrK2uCOGIiPfOOOQuhYiyXNww1dHRgfXr1wMA1qxZg87OzsnHDh06hLVr10Kn08FsNqOurg7Hjh3Dq6++Cq1Wi8997nN45JFHJl9PlG14Ll/uWltXCIBN6ESUOk28J7hcLphMpsn/VqvVCIVC0Gg0cLlcMJvNk48ZjUa4XC6Mjo5ifHwcjz76KJ588kk88MADePDBBy95766uLok+Rnbx+XyK/ezpJvW1PWcbg16I8P8XcvPntqZAi/YjZ3BDRVDWOnLx2mYKXtv04bW9IG6YMplMcLvdk/8diUSg0WhmfMztdsNsNqOwsBA33HADAOD666/Hj3/84xnfu6WlJaXis1VXV5diP3u6SX1tAy8Mo8qax/9fyM2f26s6A/jjsSEsW7YMgiDfVm4uXttMwWubPkq7th0dHbM+Fnebr62tDe3t7QCAgwcPorm5efKx1tZWdHR0wO/3w+l0oqenB83NzVi3bh1efvllAMBbb72FxsbGVD8DkSzGPEEU5rP5PFe1LSqC3R3AqRGP3KUQURaLuzK1ceNG7N+/H1u2bIEoitixYwd27dqFuro63Hjjjdi2bRu2bt0KURRxzz33QK/X4wtf+AK+/e1vY/PmzdBoNHjggQcW4rMQSW7ME+DAzhw2ObyzbxRLSowyV0NE2SpumFKpVNi+ffu0rzU0NEz+etOmTdi0adO0xwsLC/Gv//qvEpVIJI9AKAJ3IMwZUzmsqcwEs16Dd06P4hPrauQuh4iyFId2Es1iLHYun5ErU7lKNTG8853TY3KXQkRZjGGKaBZj3olz+dgzldPa6orw/uA4XH4O7ySi5DBMEc1i1M1z+ZSgbVERIiLw3hmuThFRchimiGYRO5evkD1TOW1N7cTwTp7TR0RJYpgimkWsZ6qIPVM5zZKvRVOZiZPQiShpDFNEs4itTPFuvtzXVleEd8+MQRRFuUshoizEMEU0izFvADq1CvlatdylUJqtrLFgzBNEv8MndylElIUYpohmMeYOotCglfWYEVoYyysLAABH+8dlroSIshHDFNEsRj0B3smnEMsqzBAEhikiSg7DFNEsxjxB3smnEEa9BkusRhwdcMhdChFlIYYpolmMegIMUwqyvKoARwe4MkVE88cwRTSLMW+Q23wKsryqAGfsXjgmJt8TESWKYYpoBqIoYswTQCHDlGLEmtC7uDpFRPPEMEU0A3cgjGBY5IwpBVlexTv6iCg5DFNEM+C5fMpTZs5DiUnPvikimjeGKaIZxPpmLFyZUpTlVQVcmSKieWOYIprBqIcrU0q0oqoAJ4acCIQicpdCRFmEYYpoBjyXT5mWVxYgGBZxYsgpdylElEUYpohmYHf5AQBFRq5MKQmb0IkoGQxTRDMYcvqhUQko5jafoiy2GpGvVbMJnYjmhWGKaAZDTj9KTHqoVDzkWEnUKgHLKs1cmSKieWGYIprBkNOPsgK93GWQDJZXRo+VEUVR7lKIKEswTBHNYGjchzIzw5QSLa8qgNMXwtlRr9ylEFGWYJgimoHN6UepOU/uMkgGK6osAIAj3OojogQxTBFdJBiOYMQd4MqUQi0tN0MlgE3oRJQwhimiiwxPjEVgz5Qy5evUqC81sQmdiBLGMEV0kaHxiTDFbT7FWl5ZgKP9DrnLIKIswTBFdJEhZyxMcWVKqVZVW9Dv8ME28bNARDQXhimiiww5fQC4zadkq2sLAQCHzo7JXAkRZQOGKaKLnB/3QxCAEhPDlFKtrC6ASgDeO8utPiKKj2GK6CI2pw9Wow5aNX97KJVBp0FTmRnvneHKFBHFx78tiC4yNM4ZUwSsrrXg0NkxTkInorgYpoguMuT0s/mc0FpTiFFPEGfsnIRORHNjmCK6yJCTR8kQsGaiCf09NqETURwMU0RThCMihl0B3slHWFphhk6jYt8UEcXFMEU0hd0dQDgicmAnQatWYUVVAQ7xjj4iioNhimiKyRlT3OYjAKtrCnH4nAOhcETuUogogzFMEU0xOf2c23yE6B193mAY3TaX3KUQUQZjmCKawsZz+WiK1pqJSehnuNVHRLNjmCKaIrbNV8ptPgKwxGqEOU+Dg7yjj4jmwDBFNMWQ04+CPA3ytGq5S6EMoFIJaK2x8Iw+IpoTwxTRFEPjfpQVcIuPLmitKcSxASd8wbDcpRBRhmKYIpqCAzvpYqtrChGKiDg6MC53KUSUoRimiKbgUTJ0sdW1FgDg8E4imhXDFNEEURQx5PSjnNt8NEVFQR7KzHoO7ySiWTFMEU0Y94YQCEV4Jx9NIwgCWmsKcZArU0Q0C4YpognnY9PPuTJFF7l8SRFODrsx4PDKXQoRZSCGKaIJQ5MDO7kyRdOtbyoFALxyfFjmSogoEzFMEU3guXw0m2UVZpSZ9Xj5hE3uUogoAzFMEU24cC4ft/loOkEQsL6pFK+eGEY4IspdDhFlGIYpoglD434YdGqY9Bq5S6EMdG1zCRzeIA6f4119RDQdwxTRBA7spLmsbyqFIADtx7nVR0TTMUwRTYgO7OQWH82s2KjDyioLwxQRXYJhimiCzelHaQFXpmh21zaX4N0zYxj3BeUuhYgyCMMU0YShcW7z0dyubSpFOCLitW6OSCCiCximiAC4/SG4A2Fu89Gc2hYVwaTXoP0EwxQRXRA3TEUiEdx3333YvHkztm3bhr6+vmmPP/HEE7jjjjuwadMm7Nu3b9pjb731FjZs2CBtxURpMDkWgStTNAetWoWrGqxoP26DKHJEAhFFxQ1Te/fuRSAQwJ49e3Dvvfdi586dk4/ZbDbs3r0bjz/+OB599FE8/PDDCAQCAICBgQH853/+J0KhUPqqJ5LI0HjsKBmGKZrbtU0lODvqxclht9ylEFGGiDtQp6OjA+vXrwcArFmzBp2dnZOPHTp0CGvXroVOp4NOp0NdXR2OHTuGpUuX4jvf+Q6+973v4Y477pj1vbu6uiT4CNnH5/Mp9rOnW7LX9t2TLgCAy9aPrhC3cGbCn9uoKlW0+fyJVzrxsRaLJO/Ja5s+vLbpw2t7Qdww5XK5YDKZJv9brVYjFApBo9HA5XLBbDZPPmY0GuFyubB9+3Z89rOfRXl5+Zzv3dLSkkLp2aurq0uxnz3dkr22Lw52AxjCB9uWw5ynlb6wHMCf26gWAIvaR3BiXC3Z9eC1TR9e2/RR2rXt6OiY9bG423wmkwlu94Xl7EgkAo1GM+NjbrcbWq0Wb7/9Nn74wx9i27ZtcDgcuOeee1KpnyjteoZcqLTkMUhRQjY0l+K1nhF4A2G5SyGiDBA3TLW1taG9vR0AcPDgQTQ3N08+1traio6ODvj9fjidTvT09KC1tRXPPfccdu/ejd27d8NiseAf//Ef0/cJiCRwYsiFxjJT/CcSAbhlRQW8wTBePj4kdylElAHibvNt3LgR+/fvx5YtWyCKInbs2IFdu3ahrq4ON954I7Zt24atW7dCFEXcc8890OvZwEvZJRIR0WNzYdMHauUuhbLEFUuKUWTQ4g+dg/jQykq5yyEimcUNUyqVCtu3b5/2tYaGhslfb9q0CZs2bZr19fv370+hPKL0Gxj3wRMIo6mcK1OUGI1ahZuXV+DpwwPwh8LQa9Ryl0REMuLQTlK8E+edAIDGUoYpStyHVlXA5Q/hVQ7wJFI8hilSvO6h6FgE9kzRfFzTUAJzngZ/6ByUuxQikhnDFClej82FYqMOVhP7/ShxOo0KN7WU44Wj5xEMR+Quh4hkxDBFinfivItbfJSUW1dWwOEN4kDviNylEJGMGKZI0URRjI5FYPM5JeHa5lIYdGo8c5hbfURKxjBFijbsCsDhDXJlipKSp1Xj+mVleOHoIMIRHnxMpFQMU6RobD6nVH14ZSWGXQG8dcoudylEJBOGKVK0bls0THHGFCXruqWl0GtUeJZ39REpFsMUKVr3eSdMeg0qCvLkLoWylFGvwYbmUjzbOQhR5FYfkRIxTJGiddtcaCg1QhAEuUuhLHbT8nIMjvvQNeCUuxQikgHDFCnaifMuNJaZ5S6Dstx1zaUAgH3v8+BjIiVimCLFGvcFMeT0s/mcUlZWkIeV1QV4+X2b3KUQkQwYpkixeCcfSem65jJ0nB6FwxOUu5Ss8/v3+rHjmS50D3GblLITwxQpVvf5iTv5GKZIAtcvK0U4IuKVbq5OzUcwHMF3f38EP27vxU0Pt2PTv7+O3x48B38oLHdpRAljmCLF6ra5oNOoUFtskLsUygFraotQaNBi3zGGqfl46X0bhl0BPPjJVnzz1mU4P+7DVx8/iC/9/B25SyNKmEbuAojk0j3kQn2JEWoV7+Sj1KlVAq5tKsXLx4cQiYhQ8ecqIb/qOIMSkx63r62GVq3C59fX43tPH8V/vXYKo+4Aiow6uUskiosrU6RYJ4ac7JciSV2/rBTDrgA6+x1yl5IVRlx+/LFrCLevrYJWHf3rSKUS8CerqxARgfYTXOWj7MAwRYrkDYRxdtTLMEWSurapFIIAbvUl6LcH+xGKiPjkutppX2+tKUSxUYd9xzhqgrIDwxQpUo/NBVEEmjhjiiRkNemxuqaQ86YS9KuOs2itsWBpxfTfh2qVgOuaS/HycRsPkKaswDBFinT8fPQWbK5MkdSuX1qG986Owe4OyF1KRjvS78DRgXF8al3NjI9ft6wMo54gDp4ZW+DKiOaPYYoU6cVjQ7AadQxTJLnrlpZCFIH249zqm8uvOs5Cp1bhttVVMz6+oakUapWAl7jKR1mAYYoUxxcMY9+xIdy8opx38pHkVlVbYDXquNU3h0Aogt8e7MfG5eUoNMx8t57FoMW6uiK8yL4pygIMU6Q4r/UMwx0I4+YVFXKXQjlIpRKwobkU7cdtiLDfZ0YvHhuC3R3AJz8w8xZfzHXLSnGkfxznx30LVBlRchimSHGe6zwPs16DqxuscpdCOerqxhKMeoJ4/zyPR5nJbw+eQ5lZj/WNJXM+74ZlZQDArT7KeAxTpCihcAQvdJ3H9cvKoNeo5S6HctSV9cUAgAO9IzJXknkiEREHekewobkUGvXcfwUtLTejypLHUROU8RimSFHeOjUKuzuAD63kFh+lT02RAbXF+QxTM+ixuTDqCeKyJcVxnysIAq5bVoZXu4cRCEUWoDqi5DBMkaI8d2QQeo0KG5pL5S6FctyVS6x446SdfVMXeeOkHQBwRQJhCgBuWFoGlz+Et07Z01kWUUoYpkgxRFHE80cGsb6pFEY9j6Wk9Lqy3ooxTxDHBtk3NdVbp+woM+tRl+AB41c3WqHTqDgNnTIawxQpxuFzDvQ7fNziowVx5cQNDtzqu0AURbx50o7LlxRDEBIbS2LQaXDFkmK82j2c5uqIkscwRYrxbOcg1CoBN7WUyV0KKUB1YT7qig0MU1OcHfViwOHD5Qlu8cW01RXh+HknPIFQmiojSg3DFCnGs0cGcWV98axDAomkdmV9Mfumpoj1PV22eH5hanWtBRER6Dw3no6yiFLGMEWK0D3kRK/NjVs4qJMW0FUNVji8QXQNMgQAwJsn7SjI02Bp+fwOGG+tKQQAvMdz+ihDMUyRIvyk/SS0aoFhihbUFUtifVO8Ew0A3jxlx2WLi6Ga5zFOJSY9aorycfAswxRlJoYpynmHzzrwRMcZ/Nk1S1BekCd3OaQgVYX5WGRl3xQA2Jx+9NrcCc2Xmsnq2kKuTFHGYpiinCaKIr77+yOwGnW4+4ZGucshBbqq3oo3ekcQVnjf1NsT/VLzbT6PWVNTiLOjXoy4/FKWRSQJhinKab8/NIC3+0bxN7csRUGeVu5ySIGurLdi3BdC14Cy+6bePGVHnlaFlVWWpF6/ujbaN3XorEPKsogkwTBFOcsbCOP+Z7qwsroAn1xXK3c5pFBX1nPeFBBtPl9bWwSdJrm/dlZWF0AlAAe51UcZiGGKcta/vdyDAYcP37ltBdTzbHglkkqFJQ9LSoyKDlPjviC6BsaT3uIDosM7m8vNeI9N6JSBGKYoJ50d9eDfXu7Bbaur5j3ThkhqsXlTSu2b6ugbRURMvl8qZnVNtAldFJV5HSlzMUxRzhFFEd9+shMqQcA3bl0mdzlEuLLeCqeC+6beOmmHRiVgbV1hSu+zurYQo54gzti9ElVGJA2GKco5v3n3HF5634avfWgpqgvz5S6HaLJv6vUeZW71vd47glU1Fhh0qR0wvro22rzOeVOUaRimKKeMekPY/tRRrFtUhP911WK5yyECAJQX5KFeoX1TDk8Q750Zw/rGkpTfq7ncjDytivOmKOMwTFFOeeSNEXgCYTzwidZ5T1kmSqcr6q14U4F9U/t7hhERgWubS1N+L606OlrhEFemKMMwTFHOeLZzAK/2ufHVG5vQWGaSuxyiaa6sL4bTH8LRfmX1Tb1ywgazXjM5JypVq2sLcficA6FwRJL3I5ICwxTlhDFPAN9+8ggainX4i2vr5S6H6BJXxfqmeodlrmThiKKI9uPDuLrRCq1amr9uVtcWwheM4Ph5lyTvRyQFhinKCQ88+z5GPQHcc02pZH9oE0mprCAP9aVGRR163DvsxrkxL9Y3pb7FF7O6JtqEznlTlEn4tw5lvYNnxvD4W6fxmasXo6FYL3c5RLO6st6Kt07aM3aLqtfmwk/ae7H531/H3Y+9k3J/V/txGwBggwT9UjF1xQYUGrRsQqeMktp9qkQyC0dEfPvJwyg16fG/b2rC2ZPdcpdENKur6q147I3TONI/LlkPkRSe7RzEg88dQ6/NDQBYUmLEGyftaCwz4X/f1Jz0+75yYhiLrQbUFhukKhWCIGBNbSHe7huV7D2JUsWVKcpqj73Rh85z4/j2R5fDzIOMKcNdUR+dAJ5JIxLc/hC++etDUAkCtn9sBV79+vXY93+uwx1t1finP57Aa93J9Xj5Q2G83jMiyV18F7uq3oruIReGxn2SvzdRMhimKGsNu/z4h+fex9UNVtzWWil3OURxlZnz0FCaWfOmfvFGH0Y9QTz4yVb86VWLUVMUXUX63sdWor7EiK/uOQib0z/v9+3oG4U3GJa0XyrmmomZVa8pdAgqZR6GKcpa9z9zDN5gGNs/thKCwJlSlB2uarDirVOjGdE35QuG8eP2k/hgYwna6oqmPWbUa/DDT7dh3BvEPXsOzrt/qv34MDQqAVc1WKUsGQCwvLIAhQYtXk1y1YxIagxTlJUO9I7gf945iz9fX8+ZUpRVrqy3wuUPoTMD5k398s3TGHb58ZUbGmd8fFlFQXTrr3sYj+ybXz/iKydsaFtUBJNe+tZclUrAVfVWvNY9zEOPKSMwTFHWGfcFce8T72GR1TDrXwJEmeqKJdGVGrm3+vyhMP795V5cvqQYV9TPvnq06QO1uG11Ff7lxW70jbgTem+b048j/eOS3sV3sWsaS9Dv8OHUiCdt34MoUQxTlHX+/rdHMDjuwz9uXpPywalEC63UrEdTmUn2MPXfb5/F4LgPf3VD05zPEwQB3/5ICzRqAfc/cyyh994/sf22vin18/hmE+ub2s+tPsoADFOUVX7/Xj9+/e45fOWGxkt6PIiyxdUNVhzoHYE3EJbl+wfDEfzopR6srSvENY3xe5rKC/Lwpesa8OyRQbyeQNP380cHUWTQYmWVRYpyZ7TYakCVJQ+v9TBMkfzihqlIJIL77rsPmzdvxrZt29DX1zft8SeeeAJ33HEHNm3ahH379gEA+vv78ZnPfAbbtm3DXXfdhd7e3vRUT4rSP+bFt35zGGtqC3H39dzeo+x1y4oK+IIRvHx8SJbv/5t3zuHcmBd/dUNTwjdv/Pn6elQX5mP7U0fnbEZ/65QdzxwexJ2X16X1sHFBEHB1Ywle6xlBRGGHR1PmiRum9u7di0AggD179uDee+/Fzp07Jx+z2WzYvXs3Hn/8cTz66KN4+OGHEQgE8E//9E+46667sHv3bnzhC1/Aww8/nNYPQbkvEhFx7xPvIRQR8YPNa6DhkTGUxS5fUowigxZ/6ByU5fv/7MAptFQW4Lqlifc05WnV+OaHl6FrYBz//faZGZ8TCkfwd092orowH3cvQD/jNY1WjHmCODogfzM/KVvcv5E6Ojqwfv16AMCaNWvQ2dk5+dihQ4ewdu1a6HQ6mM1m1NXV4dixY/j617+ODRs2AADC4TD0eh7xQclzeIP48mPv4PXeEXzntuVYXGKUuySilGjUKmxcXo4Xu4bgDy3sVl/3kAud58bxibbqeY8U+ciqSnxgURH+3/Pvw+kLXvL4T187hWODTvzdR5cvSD/j1Q3sm6LMEPen3eVywWS6cOu5Wq1GKBSCRqOBy+WC2WyefMxoNMLlcqG4ODrlt7e3Fw888AB++MMfzvjeXV1dqdaflXw+nyI/e0QU4Q5EMO6PYNwfRjAsotmqR5529kx/zObDzvYhDLtD+Ny6Yqwyuua8dkq9tguB11ZaKywhPOEP4fF9B9Faolqwa/uzd+1QCUCLwZ3U99y20oCvPj2Krz/2Ov7yihJoJrbyht0hPPTcGXygOh91Kju6uhbmuJdaixbPv9eHa8sCMz7On9v04bW9IG6YMplMcLsv3A4biUSg0WhmfMztdk+GqwMHDuC73/0uHnzwQdTX18/43i0tLSkVn626uroU9dlFUcSP23vx8AvH4Q9NH1SoU6uwblERrm0uxeVLiqHXqCZeA7zaPYyHnj+JCkse/vsvL0+o4Vxp13Yh8dpKq74pjP/36l4ccWhweY1uQa+j1cEAAB09SURBVK6tKIrY//uXcE1jCa5Ztyqp92gB0GHX4Gev9+GYPYJv3LoMG5eX4yu/fBdhCHho6xVYZF241eMbjofxxNtn0dC0FDrNpf8w489t+ijt2nZ0dMz6WNww1dbWhn379uHDH/4wDh48iObmC4detra24gc/+AH8fj8CgQB6enrQ3NyMAwcO4Pvf/z7+4z/+A9XV1dJ8CspKvmAY3/z1Yfzm3XO4qaUMVzWUoNioRaFBB4jA670jaD9uwwPPznzL9a0rK7DzE62w5PPcPcoteo0aN7aU4YWj5/GnLTUL8j3fOT2G03YP/urGucchxPPdP1mBDc2l2PFMF/5idwdWVheg89w4vnpj04IGKQC4urEE//V6H949PTrnvCyidIobpjZu3Ij9+/djy5YtEEURO3bswK5du1BXV4cbb7wR27Ztw9atWyGKIu655x7o9Xrs2LEDwWAQ3/jGNwAAS5Yswfbt29P+YSizDDp8+Ivdb+PQWQf+z83N+PL1jZf0aFy/rAx/++EWDI37cOisA2FRhIDonTqWfC0uW1zEo2IoZ31oZSWePNiPQ+d9WLki/d/vyXfPQa9R4ZYV5Sm9jyAIuLGlHBuaS/H4W2fwjy8cx5ISI/7yugaJKk3clfVWqARgf88IwxTJJm6YUqlUlwShhoYLv2E2bdqETZs2TXv8d7/7nUTlUbbqtbmw+ccH4PGH8ONt63Dzioo5n19WkIebluctUHVEmWFDcynytWrs73Nja5q/VzAcwdOHB3DT8nKY86RZ6dWoVbjrykX45LoahCMi8rRqSd53Piz5WqyqKcRTh/px9/WNM271EaUbf+pIcpGIiL/51SEEwxH85svXxA1SREqVr1Pj+mWleO20O+2zkl45YYPdHcDta6RvvcjTqmFMwxl8ifrK9Y3otbnx7y/3JPT8F46ex7/88QSePjSAroFx+ILyDE+l3MGzOEhyuw/0oaNvFA9vWo3mcnP8FxAp2IdWVuKZw4PoOD2KyxYXp+37PPluPwoNWlybxvPy5HLT8nJ8tLUS//JiN25dVYHGspn/3AmGI9jxTBd27T817euCAHxqXQ0e+EQr2wooKVyZIkmdHfXggWeP4drmUty+ljcfEMVzw7IyaFUC/nA4fQM8Xf4Qnj86iI+sqszZbbDv3LYC+To1vvE/h2dc5Rtx+bHt0Tewa/8p/Nk1i3Ho72/G03/1QfzLnWvxibYaPPH2WTx9eECGyikX5ObvKpKFKIr41m+iQ1133L6S/8IjSoBJr0FbVT6eOtSftu2m548MwheM4OM5/A+cUrMe3/5IC97uG8Uv3rhw7Jkoini9ZwR/8q/78e7pMTy8aTW+c9sKFORpsaLKgttWV2HnHavQWmPB3//uCEbdM8+rIpoLwxRJ5jfvnsPLx2342i1LUVNkkLscoqzxsZYCDDn9ePzN02l5/yfePoOaonysy/HDwT+5rgYfbCzBzj8cw+s9I3j4heP43G/O4M6fHIAoivjVF6/GHW2XjqHQqFV44BOtGPME8b2nj8pQOWU7himSxLDLj+1PHcW6RUXYdtViucshyiprKvNx+ZJi/PClHngD0q5OHel34ECvHduuXJTWg4czgSAI2HH7KkRE4M6fHMC/vHgC5SYt/uGTrXj+rzdgVY1l1te2VBbgS9c14NfvnMNL78tzADVlLzagkyR+uK8bTl8IO+9YBXWO/4FNJDVBEHDvxmZs/vEB/PxAHz5/7cynRiRj1/5TyNeqseWyOsneM5PVWQ145NNt6B1246OtlbCfO4mWltqEXvvlGxrxTOcgvvWbTjx3z7UwyXiHYrIiERF/6BxER98oBse96B/z4fy4D4utRvyvqxfhppZyHhSfBryilLJBhw+/eOM0PtFWjSbevUeUlCvqrfhgYwl+9HIP3P6QJO855PThdwf78akP1MBiUM4pAtcvK8PnPrgE5QXzm12n16jxwCdWod/hxcPPH09TdenT0WfH7Y/sx5cfewePvdmHYwNOGPVqXFVvxWm7B1/8+TvY8A8v4ZGXuuHwXnpQNSUv+2I3ZZxHXupGJCLi/7d353FRlnsfxz/DLjsIqOxIgqCCooILbnnQ0spSOYZmKSRath3T46me1LKkTunT0dKk7KhoqakdW4xzXFJcEBNDBURlURAEBQPZ13n+8MSTZbIN3Az83v/NMHPf35mXzvzmuq/rdz1/f8u2qBCis1swzoPJa4+z8fhl5o+5r8XH23oik6raOmYNc215uE5ioIs1U/wc+eJkJi/+qZdWbGWVXVhOxN7zfHv2Gt3MDXk/2JfJAxzuuKxbU1vH/vPX2XT8Mn+PvsDXCTnsfGaYVo6+tUcyMiVaJLuwnG0nswge5ISTtUw6F6Il/JytuL+3HZEx6dyqaNnIQUV1LVvjrnB/bzt62ppqKGHnMGuYK+XVtew+fVXpKA1KzrnFQ6uPsP98Hi+M7cUPC0czdaDj7+bH6enq8EDf7nwRPoR/zh7MpeslvPDFT9S2crPYzkKKKdEiH/2Qiho1z93f8l/RQghYEORBUXk1G45ktOg435zJIb+kitDhbhpK1nn0dbBggLMlUSeuoFa332IjKaeI6Z+ewEhfl+9fHMmCIA+MDRoeaRrjaccbj/ThYMp1Vuw93wZJOz4ppkSzZd0sY8ePWTw+2BkHyy5KxxGiQ+jrYMEDfbqz7nAax9Pym3UMtVrNhqMZeHYzY/h9svlvczwR4EL6jVJi0wqUjnJXidlFTP8kDmN9XbaHD8XNxqRJz39iiAuzh7uy4WjGHX25RPNIMSWa7cODqejoqHh2TNvvFC9ERxYxuR8u1sbM2XSKs1cLm/z82PQCUnKLCQ10lea5zTTRpweWxvpEnWh/hUZidhEzPo3D1FCPbeFDce7avCkW/zPRmzGetizZk8Sx1OYV7uI2KaZEs1wpKGXn6atM93emh4WMSgmhSVYmBkSFBWBlYsBTn50k9Xpxo59bVF7Nm98kY21iwKRW2NS4szDS12XaICf+k5xHblGF0nHqXf25jKc+O/nfQmpIswspAF0dFatDBuDa1ZjXvjpHdW2dBpN2LlJMiWb56IdUdHVUPDtaRqWEaA3dLYzYEhaAro4OMzecJLuwvMHnlFfVErbxR9JulPDBtP4Y6eu2QdKOa3qAM3VqNV+0Umf6piqvqiV8czxVNXVsDvPXyKIfMyN9XnnQi8sFZVox4b69kmJKNFnWzTJ2n85mur8zdk3s4yKEaDxXGxM2h/pTUlnD5LXH2H366l038QWoqqlj3pZ4Tmf+zD8eH8BID9s2TtvxuHQ1YZSHLV+czFR81EatVrNo5xnO595idcgA3DW4QnOslx2+TpasPpBKZU3r7A/Z0UkxJZps3eE0dFQq5o7SXJdmIcTdedub88WcIdiZGbFgxxkmfXSME+l3ToqurVPzlx0JHL54g4jJ/ZjQr4dCaTuemUNcuF5cyb7kPEVzrD2Uxrdnr/HX8b0Z09tOo8f+pQN/dmE5O37M0uixOwvp1iWaJKewnC9PZTFtsJPMlRKijfR1sGDP/OHsOZPNe9EXeDzyBL3sTFEDlTW1lFXWUlBaxWsTvJjWSbaNaSujPe1wsOxCVOwVxYrUA+fzeP8/F3jE1555rfQjdkQvG/xdrVlzMJXgQU5yibiJZGRKNMnHh9MAeGa09JUSoi3p6Kh4bIAjBxeOZvEDvXHpaoxnNzMGu1gT5N2Nv0/x0eiefuI2XR0VM4Y4E5te0KSFAJqSer2YF7cl0MfenHen+LTa6kyVSsXL4zy4XlzJlna4grG9k5Ep0Wi5RRVsO5nFFD9H6SslhEKM9HV5ZrQ7IIs/2sqfBznxwb5LbDmRybJH+rTZeYvKqpmzOR4jfR0iZw6ii0HrjhbV7w95KI0Qf2dMZKuZRpORKdFo62PSqFWreVZGpYQQnYiNqSET+nVnV/xVjW1C3ZDaOjXPb/uJqz+Xse6Jgdi30Q/YBeM8KCitYuPxy21yvo5CiinRKDeKK/k8LpPHBji0qK+JEEJoo5lDXSiurGFPQk6bnO/v0SnEXLzBm5P6MtjVuk3OCbf3hxztactnRzOoqJaVfY0lxZRolLWHUqmpU2tkJ3shhNA2fs5WePUwZ3Ps5Vbfr+9fP2WzPiadmUNcCPFv+wUF80a5U1BaxZfx0neqsaSYEg3KKSxn64lMpvg5NHn/JyGE6AhUKhUzh7iQklvM6cyfW+08Ry7dYNHOMwS4WbPkYe9WO8+9BLhZ09/Jkk9i0qmRruiNIsWUaNCag5dQo+aFsb2UjiKEEIqZ1N8eM0M9omJbZ7VbQlYhc6Picbc1JfLJQejrKvMVrVKpmDfKncybZXyfmKtIBm0jxZS4p8v5pew4dXsPPkcrmSslhOi8TAz1mDLQkb3ncskvqdTosVOvFzPrnyfpamrA5lB/LLroa/T4TTXOuxs9bUxYH5PW6pc1OwIppsQ9fbD/Ivq6KpkrJYQQwIwAZ6pq69iuwU7h2YXlzNxwEj0dHbaEBbSLbbp0dFSEj+xJYvYtjqUWNPyETk6KKfGHLuYVs+dMDk8NdW0X/7mFEEJpvbqZEXifDZ8eSaewrKrFx7tWVM4Tn8ZRUlnD5lB/XLq2n3mpj/k5YGdmWN+sWfwxKabEH1r1n4uYGOgxb5Q0BxRCiF+8NtGLWxU1vPfvCy06Tk5hOY9HnuBGcSUbZw/G295cQwk1w1BPl9BAN46m5nPuapHScdo1KabEXZ29Wkh0Ui5hgW5YmRgoHUcIIdoNrx7mPDnUhc9PZnL2amGzjnH15zKmRcZys6SKzWH+DHRpu15STTE9wBkzQz3+d/9FmTt1D1JMid9Rq9VE7E3BylifsBFuSscRQoh25y9BHtiYGvL6vxKpq2takZF1s4xp609QVFbNlqcD8HO2aqWULWdupM/zY+/jYMp1omVl3x+SYkr8zqGLN4hNL+CFsb0wN1J2RYkQQrRH5kb6vDbBizNXi9h+qvGT0ROyCpmy7jgllTVsfXoIvk6WrZhSM0KHu+Hdw5ylXydxq6Ja6TjtkhRT4g61dWre/T4FZ2tjZgS4KB1HCCHarUn97Qlws+bd6BRuljY8GX1PQjbT1sdioKfDjrlD6edo0QYpW05PV4eIyf3IL6nkveiWzRPrqKSYEnf46qdsUnKLWTTeEwM9+echhBB/RKVSsfzRvhRX1PDitp/IKSy/6+Pq6tS89+8UXtyWgK+TJXvmD8ezu1kbp20ZXydLnhzqypa4K8Rfab0O8NpKvi1FvYrqWlb+5wI+jhZM7NdD6ThCCNHueXQzY9nD3pzMuMn9Kw+x+sCl+g2CC0oq2Rp3hakfH+ejH9II8XdiS1gAXU0NFU7dPAvHe9Ld3IhXd5+jWraZuYOe0gFE+7Hx+GWuFVWw8s++6OiolI4jhBBaYeZQV8b0tmPF3vOs2neRHaeycOlqzIn0m9TWqelpa8KKx/oR4u+ESqW9n62mhnq88UgfwqPiefu780x1197XomlSTAkACsuqWPtDKmM8bRnmbqN0HCGE0CqOVsasnTGQ42n5vPN9CteKKnh2tDsT+vWgd3czrS6ifm1cn+7MHOLCxuOXOZlqyPoerjhZy1ZjUkwJAD7Yf4mSyhoWP9hb6ShCCKG1hrnb8PVzgUrHaFXLH+3LQBcrXtl9hgn/OMLbk/vxiK+90rEUJcWU4GJeMVEnrjA9wJne3dtXB14hhBDtz6MDHLCoLmDNqWJe+OInNh7LIPA+G4a4d8XP2QojfV2lI7YpKaY6ObVazfJvkzEx0GVBkKfScYQQQmiJ7mb67Jg7lA1HM9ibmMuHP6Sy+mAqBno6jO1tR2igG4NcrDrMJc57kWKqkztw/jpHLuWz5CFvrGXbGCGEEE2gp6vD3FHuzB3lzq2Kan7MuMmRS/l89VM23yfm0s/BgtBAVyb2s+/Q7XakmGqmqpo6opNyKSqvprqmjuraOnR1VEwd6IilsXYUJZU1tbz1XTLutibMHCoNOoUQQjSfuZE+Y726MdarG399wJPdp7P557EM/rL9DJuOX+GzWYM77I92KaaaIbuwnPlbT5OQ9fsNLjcczeCDaf0J6NlVgWRNs/HYZS4XlLEp1B993Y77i0EIIUTbMjbQ44khLkz3d+abszn8dedZpq47zqZQ/w65+k+KqSY6mJLHgh1nqKlVszpkAEN7dsVAVwd9PRWX8kp4aXsCIZ+cYP6Y+3hxbC/02mmRcv1WBWsOpjK2tx2jPGyVjiOEEKID0tFRMam/Aw6WXQjbdIrJ646zcfZg+thrx1Y6jdU+v+nbodo6Ne98n0LoxlPYW3Th2+cDecTXHlszQyyM9TE20MPXyZJvnw9ksp8jaw6m8uf1seSXVCod/XfUajWvfpVIVW0dr030UjqOEEKIDm6QqzU75w1FX0fFtPUniE0rUDqSRkkx1QhqtZolexL5+HAaIf7O7H52GK42Jnd9rImhHu8H+7I6ZADJ127x1GcnKW5nu2zvPp3N/vN5LBrnSU9bU6XjCCGE6AR6dTNj97PD6WFhxJzNp0jOuaV0JI2RYqoRPth/ia1xmcwb5U7E5H6N6p/xiK8962YM5EJuMeGb4+v3alJaTmE5y75Jwt/VmtBAN6XjCCGE6ES6WxixOcwfMyM9Zm88SfYfbA6tbaSYakBU7GX+ceASwQMdWfxA0/owjeltx3vBPsSmF/DStgRq69StE7KR1Go1i3edpbZOzXvBPujK/ntCCCHaWA+LLmyc7U9ZVS2zPjtJUVn7unrTHFJM3cO3Z3NY8nUSf/KyI2Jyv2Y1HntsgCOvP+RNdFIur311DrVauYJqa1wmRy7l8+oEL1y63v0ypRBCCNHaPLubsX7mQC4XlBIedYrKmvZx9aa5pJj6Az+kXGfB9jMMcrFiTYhfi1blhQW6MX+MO9t+zGL5t+cVKahSrxezYu95RvSyYUaAc5ufXwghhPi1Ye42vB/sS1zGTV744ieqa+uUjtRsUkzdxf7kPMKjTuHZ3YxPnxxMF4OW7zG0cJwns4e78tmxDCJ/LGjTgupKQSkzPo3D2ECPd6f4dIrW/kIIIdq/Sf0dWPqwN/9OyuO5z09TVaOdBZUUU78RnZjLM1vj8ba3YMvTAVgY62vkuCqViiUPeTNrmCv/On+Lt75rmxGqnMJypn8SR1VNHVufDsDeskurn1MIIYRorNnD3Vj234JqvpYWVFJM/crec9d47vPT9HWwICrMH4sumimkfqFSqVj6sDeTvMzZcDSj1Quq68UVzPg0jlvl1WwODcCzu1mrnUsIIYRorlnD3XjjkT7sS87j2a3aV1BJB3SgorqWNQcv8fHhdAY4WfLP2YMxM9JsIfULlUrF3MFdsbKyZsPRDC7mFRMxuR+OVpptr591s4ynN50it6iCqDB/+jl2rG6zQgghOpanhrmiUsGSPUlMi4zl71N86NVNOwYBOv3I1MmMm0z4xxE++iGNR/s7sDHUv9UKqV/8MkK1/NG+xF/5mQc+OMLncZkaGaWqqK5l9YFL/GnVYbJ+LuPTpwYxyNVaA6mFEEKI1vXkUFfWhAwgI7+UiauPsvrAJa0YpeqUI1NqtZoLecVsjr3C53GZOFp1YXOoPyPbcI86lUrFzCEujPawZfGus7z61Tm+O5dDWKAbgffZYqDXtDq3rk7NoYvXeeObZK4UlDHRpwf/M9GLHhYyR0oIIYT2eNjXnqHuXXnjm2RW7bvI3nPXWBDkwUgP20Y1zVZCg8VUXV0dy5Yt48KFCxgYGPDWW2/h4uJS//cdO3awbds29PT0eOaZZxgzZgw3b95k4cKFVFRUYGdnR0REBF26KPelrlaruVVeQ1p+CfuS84hOzCUjvxQdFYQOd+PlcR6YGCpTVzpZG7P16QA+P5nJu//d+8/MSI8g72480Kc7PW1N6W5hhOlv8lXV1JF3q4LY9AKOpeZzLDWf/JIq3G1N2BIWQGAvG0VejxBCCNFSNqaGrAkZwCRfe17fk0h4VDwmBrqM6W3Hg317MMDZkm7mRu2m+XSDFcT+/fupqqpi+/btJCQk8M4777Bu3ToAbty4QVRUFLt27aKyspLp06czfPhw1q5dy0MPPcTkyZOJjIxk+/btzJo1q7Vfyx0qqmv5266zJF+7RU5hBSWVNQDo6qgY5t6VsEA3xvXphp2ZUZvmuhuVSsWMABemDnTkWGo+353NZV9yLrtPZ9c/xtRQj66mBpRX1XKropqK6v8f9rQxNSTwPhtG9LLlYV/7Jo9qCSGEEO3Rn7y7MdLDltj0AqITb383fnv2GnD7+7y7uREOll1YON4TfzflprSo1A1M1ImIiMDHx4eJEycCMGLECI4cOQLAgQMHOHz4MG+++SYA8+fPZ+7cuSxdupTIyEhsbW1JSUlh1apVREZG3nHc+Pj41ng9QgghhBCtYuDAgXe9v8GRqZKSEkxNTetv6+rqUlNTg56eHiUlJZiZ/f9MexMTE0pKSu6438TEhOLi4kYHEkIIIYTQJg1eDzI1NaW0tLT+dl1dHXp6enf9W2lpKWZmZnfcX1pairm5uaZzCyGEEEK0Cw0WU35+fsTExACQkJCAh4dH/d98fHyIj4+nsrKS4uJi0tLS8PDwwM/Pj8OHDwMQExMjo1BCCCGE6LAanDP1y2q+ixcvolarWbFiBTExMTg7OzN27Fh27NjB9u3bUavVzJ07l/Hjx5Ofn8/ixYspLS3FysqKlStXYmys2aaUQgghhBDtQYPFlNCsffv2ER0dzcqVK4Hbo31vv/02urq6BAYG8txzzymcULup1WpGjhyJq6srAP379+fll19WNpSWa6g9imiZRx99tH6OqaOjIxEREQon0n5nzpzh/fffJyoqiitXrvC3v/0NlUpFr169WLp0KTo6suK5OX79viYlJTFv3rz6z9qQkBAmTJigbEAFdcqmnUp56623OHr0KF5eXvX3LV26lDVr1uDk5ER4eDhJSUn06dNHwZTaLTMzkz59+vDxxx8rHaXDuFd7FNEylZWVAERFRSmcpOP45JNP+Prrr+t7G0ZERPDSSy8REBDAkiVLOHDgAEFBQQqn1D6/fV+Tk5OZPXs2oaGhCidrH6Q8b0N+fn4sW7as/nZJSQlVVVU4OzujUqkIDAwkNjZWuYAdQFJSEnl5ecycOZM5c+aQnp6udCStFx8fz4gRI4DbI32JiYkKJ+o4UlJSKC8vJzQ0lCeffJKEhASlI2k9Z2dn1qxZU387KSkJf39/AEaOHMnx48eViqbVfvu+JiYmcujQIWbMmMGrr75KSUmJgumUJyNTreDLL79k06ZNd9y3YsUKJkyYQFxcXP19v207YWJiQlZWVpvl1HZ3e5+XLFlCeHg4Dz74IKdOnWLRokXs2rVLoYQdw73ao4iWMTIyIiwsjODgYC5fvsycOXOIjo6W97YFxo8fz9WrV+tvq9VqVKrbXbL/qFWPaNhv31cfHx+Cg4Pp27cv69at46OPPmLx4sUKJlSW/I9tBcHBwQQHBzf4uLu1lpA2Eo13t/e5vLwcXd3bezcNGjSIvLy8Oz5MRdPdqz2KaBk3NzdcXFxQqVS4ublhaWnJjRs36NGjh9LROoxfz4+Sz1jNCQoKqn8vg4KCWL58ucKJlCWX+RRkamqKvr4+mZmZqNVqjh49yqBBg5SOpdU+/PDD+tGqlJQU7O3tpZBqoXu1RxEts3PnTt555x0A8vLyKCkpwda27TZc7wy8vb3rrwjExMTIZ6yGhIWFcfbsWQBiY2M7/Vxf+XmpsDfeeIOFCxdSW1tLYGAgvr6+SkfSauHh4SxatIjDhw+jq6srK6M0ICgoiGPHjvH444/Xt0cRmjF16lReeeUVQkJCUKlUrFixQkb9NGzx4sW8/vrrrFq1ip49ezJ+/HilI3UIy5YtY/ny5ejr62NjY9PpR6akNYIQQgghRAvIZT4hhBBCiBaQYkoIIYQQogWkmBJCCCGEaAEppoQQQgghWkCKKSGEEEKIFpBiSgghhBCiBaSYEkIIIYRogf8DC6gxhGQG25cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the distribution of erros\n",
    "y_error1  = y_test-y_pred2\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.kdeplot(np.array(y_error1), bw=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients of best fit line\n",
      "[  2.47428274   2.28718653   1.17362182   2.42869823  -7.27349978\n",
      "  17.15725412   1.93884414  -8.23691192   3.25900927  -5.28715235\n",
      "  -6.38691764   5.55322768 -24.47741277]\n",
      "Intercept of the Best fit line : [28.8867301] \n"
     ]
    }
   ],
   "source": [
    "#getting the intercept and coefficients of the fitted linear model\n",
    "print(\"Coefficients of best fit line\")\n",
    "print(sgd_reg.coef_)\n",
    "print(\"Intercept of the Best fit line : {} \".format( sgd_reg.intercept_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>2.474283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>2.287187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>1.173622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>2.428698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>-7.273500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>17.157254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>1.938844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-8.236912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>3.259009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>-5.287152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>-6.386918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5.553228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>-24.477413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Coefficients\n",
       "CRIM       2.474283\n",
       "ZN         2.287187\n",
       "INDUS      1.173622\n",
       "CHAS       2.428698\n",
       "NOX       -7.273500\n",
       "RM        17.157254\n",
       "AGE        1.938844\n",
       "DIS       -8.236912\n",
       "RAD        3.259009\n",
       "TAX       -5.287152\n",
       "PT        -6.386918\n",
       "B          5.553228\n",
       "LSTAT    -24.477413"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the coefficients of each column\n",
    "coefficients = pd.DataFrame(sgd_reg.coef_,X_data.columns)\n",
    "coefficients.columns = ['Coefficients']\n",
    "coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+-------------------+\n",
      "| metric/parameter |       result      |\n",
      "+------------------+-------------------+\n",
      "|      Model       |     Regression    |\n",
      "|    algorithm     | Linear-Regression |\n",
      "|       MSE        |        23.8       |\n",
      "|       RMSE       |        4.88       |\n",
      "+------------------+-------------------+\n",
      "+------------------+---------------+\n",
      "| metric/parameter |     result    |\n",
      "+------------------+---------------+\n",
      "|      Model       |   Regression  |\n",
      "|    algorithm     | SGD-Regressor |\n",
      "|       MSE        |      24.8     |\n",
      "|       RMSE       |      4.98     |\n",
      "+------------------+---------------+\n",
      "+------------------+------------+\n",
      "| metric/parameter |   result   |\n",
      "+------------------+------------+\n",
      "|      Model       | Regression |\n",
      "|    algorithm     |    SGD     |\n",
      "|       MSE        |   23.05    |\n",
      "|       RMSE       |    4.8     |\n",
      "+------------------+------------+\n"
     ]
    }
   ],
   "source": [
    "#reporting the results in a table\n",
    "from prettytable import PrettyTable\n",
    "\n",
    "\n",
    "t= PrettyTable()\n",
    "\n",
    "t.field_names =[\"metric/parameter\" , \"result\"]\n",
    "\n",
    "t.add_row ([\"Model\" ,\"Regression\"])\n",
    "t.add_row ([\"algorithm\" ,\"Linear-Regression\"])\n",
    "t.add_row ([\"MSE\" ,round(mse,2)])\n",
    "t.add_row ([\"RMSE\" ,round(rmse,2)])\n",
    "print(t)\n",
    "    \n",
    "    \n",
    "t1= PrettyTable()\n",
    "\n",
    "t1.field_names =[\"metric/parameter\" , \"result\"]\n",
    "\n",
    "t1.add_row ([\"Model\" ,\"Regression\"])\n",
    "t1.add_row ([\"algorithm\" ,\"SGD-Regressor\"])\n",
    "t1.add_row ([\"MSE\" ,round(mse1,2)])\n",
    "t1.add_row ([\"RMSE\" ,round(rmse1,2)])\n",
    "\n",
    "print(t1)\n",
    "        \n",
    "t2= PrettyTable()\n",
    "\n",
    "t2.field_names =[\"metric/parameter\" , \"result\"]\n",
    "\n",
    "t2.add_row ([\"Model\" ,\"Regression\"])\n",
    "t2.add_row ([\"algorithm\" ,\"SGD\"])\n",
    "t2.add_row ([\"MSE\" ,round(mse_sgd,2)])\n",
    "t2.add_row ([\"RMSE\" ,round(rmse_sgd,2)])\n",
    "print(t2)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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