class_1_LinReg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing All packages and Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets #contains all the datasets to work within sklearn package\n",
    "\n",
    "import pandas as pd #scientific libraries to perform operation on dataset\n",
    "import numpy as np #scientific libraries to perform operation on dataset\n",
    "\n",
    "from sklearn.linear_model import LinearRegression# Importing algorithms to work for prediction\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split # library to divide the data into training set\n",
    "# and testing set\n",
    "\n",
    "from sklearn.metrics import mean_squared_error,r2_score #libraries to measure the accuracy and error in prediction\n",
    "\n",
    "import matplotlib.pyplot as plt # libraries to plot graph and visualize the data distribution \n",
    "\n",
    "from sklearn.utils import shuffle #librries to shuffle the data to provide randomization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# loading the particular dataset from given set of datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston=datasets.load_boston() #press tab after datasets. to see number of datsets present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.keys() #to get key items that has certain values(assume like title and description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. 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",
      "        - PTRATIO  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",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston['DESCR']) # way to determine the value of particular key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</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>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</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>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</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>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.02985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.430</td>\n",
       "      <td>58.7</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.12</td>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.08829</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.012</td>\n",
       "      <td>66.6</td>\n",
       "      <td>5.5605</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>395.60</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.14455</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.172</td>\n",
       "      <td>96.1</td>\n",
       "      <td>5.9505</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>19.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.21124</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>5.631</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.0821</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.63</td>\n",
       "      <td>29.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.17004</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.004</td>\n",
       "      <td>85.9</td>\n",
       "      <td>6.5921</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.71</td>\n",
       "      <td>17.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.22489</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.377</td>\n",
       "      <td>94.3</td>\n",
       "      <td>6.3467</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>392.52</td>\n",
       "      <td>20.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.11747</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.009</td>\n",
       "      <td>82.9</td>\n",
       "      <td>6.2267</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>13.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.09378</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>5.889</td>\n",
       "      <td>39.0</td>\n",
       "      <td>5.4509</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>390.50</td>\n",
       "      <td>15.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.62976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.949</td>\n",
       "      <td>61.8</td>\n",
       "      <td>4.7075</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.63796</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.096</td>\n",
       "      <td>84.5</td>\n",
       "      <td>4.4619</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>380.02</td>\n",
       "      <td>10.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.62739</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.834</td>\n",
       "      <td>56.5</td>\n",
       "      <td>4.4986</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>395.62</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.05393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.935</td>\n",
       "      <td>29.3</td>\n",
       "      <td>4.4986</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>386.85</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.78420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.990</td>\n",
       "      <td>81.7</td>\n",
       "      <td>4.2579</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>386.75</td>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.80271</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.456</td>\n",
       "      <td>36.6</td>\n",
       "      <td>3.7965</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>288.99</td>\n",
       "      <td>11.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.72580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.727</td>\n",
       "      <td>69.5</td>\n",
       "      <td>3.7965</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>390.95</td>\n",
       "      <td>11.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.25179</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.570</td>\n",
       "      <td>98.1</td>\n",
       "      <td>3.7979</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>376.57</td>\n",
       "      <td>21.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.85204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.965</td>\n",
       "      <td>89.2</td>\n",
       "      <td>4.0123</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>392.53</td>\n",
       "      <td>13.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.23247</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.142</td>\n",
       "      <td>91.7</td>\n",
       "      <td>3.9769</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>18.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.98843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.813</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4.0952</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>394.54</td>\n",
       "      <td>19.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.75026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.924</td>\n",
       "      <td>94.1</td>\n",
       "      <td>4.3996</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>394.33</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.84054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.599</td>\n",
       "      <td>85.7</td>\n",
       "      <td>4.4546</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>303.42</td>\n",
       "      <td>16.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.67191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.813</td>\n",
       "      <td>90.3</td>\n",
       "      <td>4.6820</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>376.88</td>\n",
       "      <td>14.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.95577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.047</td>\n",
       "      <td>88.8</td>\n",
       "      <td>4.4534</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>306.38</td>\n",
       "      <td>17.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.77299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.495</td>\n",
       "      <td>94.4</td>\n",
       "      <td>4.4547</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>387.94</td>\n",
       "      <td>12.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.00245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.674</td>\n",
       "      <td>87.3</td>\n",
       "      <td>4.2390</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>380.23</td>\n",
       "      <td>11.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>4.87141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.614</td>\n",
       "      <td>6.484</td>\n",
       "      <td>93.6</td>\n",
       "      <td>2.3053</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>396.21</td>\n",
       "      <td>18.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>15.02340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.614</td>\n",
       "      <td>5.304</td>\n",
       "      <td>97.3</td>\n",
       "      <td>2.1007</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>349.48</td>\n",
       "      <td>24.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>10.23300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.614</td>\n",
       "      <td>6.185</td>\n",
       "      <td>96.7</td>\n",
       "      <td>2.1705</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>379.70</td>\n",
       "      <td>18.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>14.33370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.614</td>\n",
       "      <td>6.229</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.9512</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>383.32</td>\n",
       "      <td>13.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>5.82401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.532</td>\n",
       "      <td>6.242</td>\n",
       "      <td>64.7</td>\n",
       "      <td>3.4242</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>5.70818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.532</td>\n",
       "      <td>6.750</td>\n",
       "      <td>74.9</td>\n",
       "      <td>3.3317</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>393.07</td>\n",
       "      <td>7.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>5.73116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.532</td>\n",
       "      <td>7.061</td>\n",
       "      <td>77.0</td>\n",
       "      <td>3.4106</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>395.28</td>\n",
       "      <td>7.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>2.81838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.532</td>\n",
       "      <td>5.762</td>\n",
       "      <td>40.3</td>\n",
       "      <td>4.0983</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>392.92</td>\n",
       "      <td>10.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>2.37857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.583</td>\n",
       "      <td>5.871</td>\n",
       "      <td>41.9</td>\n",
       "      <td>3.7240</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>370.73</td>\n",
       "      <td>13.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>3.67367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.583</td>\n",
       "      <td>6.312</td>\n",
       "      <td>51.9</td>\n",
       "      <td>3.9917</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>388.62</td>\n",
       "      <td>10.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>5.69175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.583</td>\n",
       "      <td>6.114</td>\n",
       "      <td>79.8</td>\n",
       "      <td>3.5459</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>392.68</td>\n",
       "      <td>14.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>4.83567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.583</td>\n",
       "      <td>5.905</td>\n",
       "      <td>53.2</td>\n",
       "      <td>3.1523</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>388.22</td>\n",
       "      <td>11.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>0.15086</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.609</td>\n",
       "      <td>5.454</td>\n",
       "      <td>92.7</td>\n",
       "      <td>1.8209</td>\n",
       "      <td>4.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>395.09</td>\n",
       "      <td>18.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>0.18337</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.609</td>\n",
       "      <td>5.414</td>\n",
       "      <td>98.3</td>\n",
       "      <td>1.7554</td>\n",
       "      <td>4.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>344.05</td>\n",
       "      <td>23.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>0.20746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.609</td>\n",
       "      <td>5.093</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.8226</td>\n",
       "      <td>4.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>318.43</td>\n",
       "      <td>29.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>0.10574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.609</td>\n",
       "      <td>5.983</td>\n",
       "      <td>98.8</td>\n",
       "      <td>1.8681</td>\n",
       "      <td>4.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>390.11</td>\n",
       "      <td>18.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>0.11132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.609</td>\n",
       "      <td>5.983</td>\n",
       "      <td>83.5</td>\n",
       "      <td>2.1099</td>\n",
       "      <td>4.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>396.90</td>\n",
       "      <td>13.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>0.17331</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585</td>\n",
       "      <td>5.707</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.3817</td>\n",
       "      <td>6.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>12.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>0.27957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585</td>\n",
       "      <td>5.926</td>\n",
       "      <td>42.6</td>\n",
       "      <td>2.3817</td>\n",
       "      <td>6.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>13.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0.17899</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585</td>\n",
       "      <td>5.670</td>\n",
       "      <td>28.8</td>\n",
       "      <td>2.7986</td>\n",
       "      <td>6.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>393.29</td>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>0.28960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585</td>\n",
       "      <td>5.390</td>\n",
       "      <td>72.9</td>\n",
       "      <td>2.7986</td>\n",
       "      <td>6.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>21.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0.26838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585</td>\n",
       "      <td>5.794</td>\n",
       "      <td>70.6</td>\n",
       "      <td>2.8927</td>\n",
       "      <td>6.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>14.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>0.23912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585</td>\n",
       "      <td>6.019</td>\n",
       "      <td>65.3</td>\n",
       "      <td>2.4091</td>\n",
       "      <td>6.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>12.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>0.17783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585</td>\n",
       "      <td>5.569</td>\n",
       "      <td>73.5</td>\n",
       "      <td>2.3999</td>\n",
       "      <td>6.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>395.77</td>\n",
       "      <td>15.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.22438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585</td>\n",
       "      <td>6.027</td>\n",
       "      <td>79.7</td>\n",
       "      <td>2.4982</td>\n",
       "      <td>6.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>14.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CRIM    ZN  INDUS  CHAS    NOX     RM    AGE     DIS   RAD    TAX  \\\n",
       "0     0.00632  18.0   2.31   0.0  0.538  6.575   65.2  4.0900   1.0  296.0   \n",
       "1     0.02731   0.0   7.07   0.0  0.469  6.421   78.9  4.9671   2.0  242.0   \n",
       "2     0.02729   0.0   7.07   0.0  0.469  7.185   61.1  4.9671   2.0  242.0   \n",
       "3     0.03237   0.0   2.18   0.0  0.458  6.998   45.8  6.0622   3.0  222.0   \n",
       "4     0.06905   0.0   2.18   0.0  0.458  7.147   54.2  6.0622   3.0  222.0   \n",
       "5     0.02985   0.0   2.18   0.0  0.458  6.430   58.7  6.0622   3.0  222.0   \n",
       "6     0.08829  12.5   7.87   0.0  0.524  6.012   66.6  5.5605   5.0  311.0   \n",
       "7     0.14455  12.5   7.87   0.0  0.524  6.172   96.1  5.9505   5.0  311.0   \n",
       "8     0.21124  12.5   7.87   0.0  0.524  5.631  100.0  6.0821   5.0  311.0   \n",
       "9     0.17004  12.5   7.87   0.0  0.524  6.004   85.9  6.5921   5.0  311.0   \n",
       "10    0.22489  12.5   7.87   0.0  0.524  6.377   94.3  6.3467   5.0  311.0   \n",
       "11    0.11747  12.5   7.87   0.0  0.524  6.009   82.9  6.2267   5.0  311.0   \n",
       "12    0.09378  12.5   7.87   0.0  0.524  5.889   39.0  5.4509   5.0  311.0   \n",
       "13    0.62976   0.0   8.14   0.0  0.538  5.949   61.8  4.7075   4.0  307.0   \n",
       "14    0.63796   0.0   8.14   0.0  0.538  6.096   84.5  4.4619   4.0  307.0   \n",
       "15    0.62739   0.0   8.14   0.0  0.538  5.834   56.5  4.4986   4.0  307.0   \n",
       "16    1.05393   0.0   8.14   0.0  0.538  5.935   29.3  4.4986   4.0  307.0   \n",
       "17    0.78420   0.0   8.14   0.0  0.538  5.990   81.7  4.2579   4.0  307.0   \n",
       "18    0.80271   0.0   8.14   0.0  0.538  5.456   36.6  3.7965   4.0  307.0   \n",
       "19    0.72580   0.0   8.14   0.0  0.538  5.727   69.5  3.7965   4.0  307.0   \n",
       "20    1.25179   0.0   8.14   0.0  0.538  5.570   98.1  3.7979   4.0  307.0   \n",
       "21    0.85204   0.0   8.14   0.0  0.538  5.965   89.2  4.0123   4.0  307.0   \n",
       "22    1.23247   0.0   8.14   0.0  0.538  6.142   91.7  3.9769   4.0  307.0   \n",
       "23    0.98843   0.0   8.14   0.0  0.538  5.813  100.0  4.0952   4.0  307.0   \n",
       "24    0.75026   0.0   8.14   0.0  0.538  5.924   94.1  4.3996   4.0  307.0   \n",
       "25    0.84054   0.0   8.14   0.0  0.538  5.599   85.7  4.4546   4.0  307.0   \n",
       "26    0.67191   0.0   8.14   0.0  0.538  5.813   90.3  4.6820   4.0  307.0   \n",
       "27    0.95577   0.0   8.14   0.0  0.538  6.047   88.8  4.4534   4.0  307.0   \n",
       "28    0.77299   0.0   8.14   0.0  0.538  6.495   94.4  4.4547   4.0  307.0   \n",
       "29    1.00245   0.0   8.14   0.0  0.538  6.674   87.3  4.2390   4.0  307.0   \n",
       "..        ...   ...    ...   ...    ...    ...    ...     ...   ...    ...   \n",
       "476   4.87141   0.0  18.10   0.0  0.614  6.484   93.6  2.3053  24.0  666.0   \n",
       "477  15.02340   0.0  18.10   0.0  0.614  5.304   97.3  2.1007  24.0  666.0   \n",
       "478  10.23300   0.0  18.10   0.0  0.614  6.185   96.7  2.1705  24.0  666.0   \n",
       "479  14.33370   0.0  18.10   0.0  0.614  6.229   88.0  1.9512  24.0  666.0   \n",
       "480   5.82401   0.0  18.10   0.0  0.532  6.242   64.7  3.4242  24.0  666.0   \n",
       "481   5.70818   0.0  18.10   0.0  0.532  6.750   74.9  3.3317  24.0  666.0   \n",
       "482   5.73116   0.0  18.10   0.0  0.532  7.061   77.0  3.4106  24.0  666.0   \n",
       "483   2.81838   0.0  18.10   0.0  0.532  5.762   40.3  4.0983  24.0  666.0   \n",
       "484   2.37857   0.0  18.10   0.0  0.583  5.871   41.9  3.7240  24.0  666.0   \n",
       "485   3.67367   0.0  18.10   0.0  0.583  6.312   51.9  3.9917  24.0  666.0   \n",
       "486   5.69175   0.0  18.10   0.0  0.583  6.114   79.8  3.5459  24.0  666.0   \n",
       "487   4.83567   0.0  18.10   0.0  0.583  5.905   53.2  3.1523  24.0  666.0   \n",
       "488   0.15086   0.0  27.74   0.0  0.609  5.454   92.7  1.8209   4.0  711.0   \n",
       "489   0.18337   0.0  27.74   0.0  0.609  5.414   98.3  1.7554   4.0  711.0   \n",
       "490   0.20746   0.0  27.74   0.0  0.609  5.093   98.0  1.8226   4.0  711.0   \n",
       "491   0.10574   0.0  27.74   0.0  0.609  5.983   98.8  1.8681   4.0  711.0   \n",
       "492   0.11132   0.0  27.74   0.0  0.609  5.983   83.5  2.1099   4.0  711.0   \n",
       "493   0.17331   0.0   9.69   0.0  0.585  5.707   54.0  2.3817   6.0  391.0   \n",
       "494   0.27957   0.0   9.69   0.0  0.585  5.926   42.6  2.3817   6.0  391.0   \n",
       "495   0.17899   0.0   9.69   0.0  0.585  5.670   28.8  2.7986   6.0  391.0   \n",
       "496   0.28960   0.0   9.69   0.0  0.585  5.390   72.9  2.7986   6.0  391.0   \n",
       "497   0.26838   0.0   9.69   0.0  0.585  5.794   70.6  2.8927   6.0  391.0   \n",
       "498   0.23912   0.0   9.69   0.0  0.585  6.019   65.3  2.4091   6.0  391.0   \n",
       "499   0.17783   0.0   9.69   0.0  0.585  5.569   73.5  2.3999   6.0  391.0   \n",
       "500   0.22438   0.0   9.69   0.0  0.585  6.027   79.7  2.4982   6.0  391.0   \n",
       "501   0.06263   0.0  11.93   0.0  0.573  6.593   69.1  2.4786   1.0  273.0   \n",
       "502   0.04527   0.0  11.93   0.0  0.573  6.120   76.7  2.2875   1.0  273.0   \n",
       "503   0.06076   0.0  11.93   0.0  0.573  6.976   91.0  2.1675   1.0  273.0   \n",
       "504   0.10959   0.0  11.93   0.0  0.573  6.794   89.3  2.3889   1.0  273.0   \n",
       "505   0.04741   0.0  11.93   0.0  0.573  6.030   80.8  2.5050   1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "0       15.3  396.90   4.98  \n",
       "1       17.8  396.90   9.14  \n",
       "2       17.8  392.83   4.03  \n",
       "3       18.7  394.63   2.94  \n",
       "4       18.7  396.90   5.33  \n",
       "5       18.7  394.12   5.21  \n",
       "6       15.2  395.60  12.43  \n",
       "7       15.2  396.90  19.15  \n",
       "8       15.2  386.63  29.93  \n",
       "9       15.2  386.71  17.10  \n",
       "10      15.2  392.52  20.45  \n",
       "11      15.2  396.90  13.27  \n",
       "12      15.2  390.50  15.71  \n",
       "13      21.0  396.90   8.26  \n",
       "14      21.0  380.02  10.26  \n",
       "15      21.0  395.62   8.47  \n",
       "16      21.0  386.85   6.58  \n",
       "17      21.0  386.75  14.67  \n",
       "18      21.0  288.99  11.69  \n",
       "19      21.0  390.95  11.28  \n",
       "20      21.0  376.57  21.02  \n",
       "21      21.0  392.53  13.83  \n",
       "22      21.0  396.90  18.72  \n",
       "23      21.0  394.54  19.88  \n",
       "24      21.0  394.33  16.30  \n",
       "25      21.0  303.42  16.51  \n",
       "26      21.0  376.88  14.81  \n",
       "27      21.0  306.38  17.28  \n",
       "28      21.0  387.94  12.80  \n",
       "29      21.0  380.23  11.98  \n",
       "..       ...     ...    ...  \n",
       "476     20.2  396.21  18.68  \n",
       "477     20.2  349.48  24.91  \n",
       "478     20.2  379.70  18.03  \n",
       "479     20.2  383.32  13.11  \n",
       "480     20.2  396.90  10.74  \n",
       "481     20.2  393.07   7.74  \n",
       "482     20.2  395.28   7.01  \n",
       "483     20.2  392.92  10.42  \n",
       "484     20.2  370.73  13.34  \n",
       "485     20.2  388.62  10.58  \n",
       "486     20.2  392.68  14.98  \n",
       "487     20.2  388.22  11.45  \n",
       "488     20.1  395.09  18.06  \n",
       "489     20.1  344.05  23.97  \n",
       "490     20.1  318.43  29.68  \n",
       "491     20.1  390.11  18.07  \n",
       "492     20.1  396.90  13.35  \n",
       "493     19.2  396.90  12.01  \n",
       "494     19.2  396.90  13.59  \n",
       "495     19.2  393.29  17.60  \n",
       "496     19.2  396.90  21.14  \n",
       "497     19.2  396.90  14.10  \n",
       "498     19.2  396.90  12.92  \n",
       "499     19.2  395.77  15.10  \n",
       "500     19.2  396.90  14.33  \n",
       "501     21.0  391.99   9.67  \n",
       "502     21.0  396.90   9.08  \n",
       "503     21.0  396.90   5.64  \n",
       "504     21.0  393.45   6.48  \n",
       "505     21.0  396.90   7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=pd.DataFrame(boston.data,columns=boston.feature_names) # Creating a dataframe to perform operations on data \n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</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",
       "    </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>356.674032</td>\n",
       "      <td>12.653063</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>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</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",
       "    </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>375.377500</td>\n",
       "      <td>6.950000</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>391.440000</td>\n",
       "      <td>11.360000</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>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</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>396.900000</td>\n",
       "      <td>37.970000</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     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           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  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \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  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT  \n",
       "count  506.000000  \n",
       "mean    12.653063  \n",
       "std      7.141062  \n",
       "min      1.730000  \n",
       "25%      6.950000  \n",
       "50%     11.360000  \n",
       "75%     16.955000  \n",
       "max     37.970000  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe() #getting overall info of data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=boston.target # assigning the target value or end feature to be predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# creating new dataframe for single regression i.e how one feature affects target value Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>29.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>13.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>18.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>19.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>17.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>18.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>24.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>18.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>13.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>7.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>7.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>10.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>13.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>10.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>14.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>11.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>18.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>23.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>29.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>18.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>13.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>12.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>13.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>21.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>14.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>12.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>15.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>14.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     LSTAT\n",
       "0     4.98\n",
       "1     9.14\n",
       "2     4.03\n",
       "3     2.94\n",
       "4     5.33\n",
       "5     5.21\n",
       "6    12.43\n",
       "7    19.15\n",
       "8    29.93\n",
       "9    17.10\n",
       "10   20.45\n",
       "11   13.27\n",
       "12   15.71\n",
       "13    8.26\n",
       "14   10.26\n",
       "15    8.47\n",
       "16    6.58\n",
       "17   14.67\n",
       "18   11.69\n",
       "19   11.28\n",
       "20   21.02\n",
       "21   13.83\n",
       "22   18.72\n",
       "23   19.88\n",
       "24   16.30\n",
       "25   16.51\n",
       "26   14.81\n",
       "27   17.28\n",
       "28   12.80\n",
       "29   11.98\n",
       "..     ...\n",
       "476  18.68\n",
       "477  24.91\n",
       "478  18.03\n",
       "479  13.11\n",
       "480  10.74\n",
       "481   7.74\n",
       "482   7.01\n",
       "483  10.42\n",
       "484  13.34\n",
       "485  10.58\n",
       "486  14.98\n",
       "487  11.45\n",
       "488  18.06\n",
       "489  23.97\n",
       "490  29.68\n",
       "491  18.07\n",
       "492  13.35\n",
       "493  12.01\n",
       "494  13.59\n",
       "495  17.60\n",
       "496  21.14\n",
       "497  14.10\n",
       "498  12.92\n",
       "499  15.10\n",
       "500  14.33\n",
       "501   9.67\n",
       "502   9.08\n",
       "503   5.64\n",
       "504   6.48\n",
       "505   7.88\n",
       "\n",
       "[506 rows x 1 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new=pd.DataFrame(X['LSTAT'])\n",
    "X_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 21.,  55.,  82., 154.,  84.,  41.,  30.,   8.,  10.,  21.]),\n",
       " array([ 5. ,  9.5, 14. , 18.5, 23. , 27.5, 32. , 36.5, 41. , 45.5, 50. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Regression\n",
    "\n",
    "creating a model of our algorithm,\n",
    "dividing the data into training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>14.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>34.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>18.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>15.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>10.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>13.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>6.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>23.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>7.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>11.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>9.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>6.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>24.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>4.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>5.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>34.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>8.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>17.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>18.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>7.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>7.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>10.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>17.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>7.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>9.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>6.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>8.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>21.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>18.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>24.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>12.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>13.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>14.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>12.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>25.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>17.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>10.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>5.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>19.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>30.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>13.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>13.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>12.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>23.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>4.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     LSTAT\n",
       "469  14.76\n",
       "141  34.41\n",
       "454  18.71\n",
       "426  15.69\n",
       "362  10.19\n",
       "162   1.92\n",
       "216  13.51\n",
       "324   6.12\n",
       "238   6.36\n",
       "406  23.34\n",
       "81    7.22\n",
       "487  11.45\n",
       "314   9.28\n",
       "187   6.68\n",
       "425  24.39\n",
       "298   4.97\n",
       "179   5.04\n",
       "438  34.02\n",
       "53    8.43\n",
       "395  17.12\n",
       "139  18.46\n",
       "267   7.44\n",
       "278   7.19\n",
       "66   10.24\n",
       "424  17.16\n",
       "259   6.90\n",
       "365   7.12\n",
       "220   9.71\n",
       "99    6.19\n",
       "277   4.16\n",
       "..     ...\n",
       "6    12.43\n",
       "250   5.90\n",
       "311   5.98\n",
       "75    8.94\n",
       "0     4.98\n",
       "428  21.52\n",
       "391  18.76\n",
       "383  24.56\n",
       "432  12.03\n",
       "68   13.09\n",
       "486  14.98\n",
       "167  12.14\n",
       "392  25.68\n",
       "218  17.92\n",
       "42    5.81\n",
       "366  14.00\n",
       "345  10.53\n",
       "72    5.52\n",
       "23   19.88\n",
       "398  30.59\n",
       "185  13.15\n",
       "348   5.99\n",
       "103  13.44\n",
       "407  12.13\n",
       "211  23.98\n",
       "67    8.10\n",
       "502   9.08\n",
       "25   16.51\n",
       "196   4.08\n",
       "175   5.33\n",
       "\n",
       "[354 rows x 1 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression()\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X_new,Y,test_size=0.3,random_state=7)#dividing data into 70% train and 30% test on random seed of 7\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)# give data to model for learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.26393949, 24.79620654, 22.71245911, 26.94536308, 15.39598272,\n",
       "       16.19023623, 22.41344603, 26.21651869, 30.29991611, 18.63840585,\n",
       "        6.05182386, 28.72075326, 19.16167875, 20.13347127, 31.75760489,\n",
       "       21.24542617, 25.78668738, 30.12237709,  8.77097409, 24.94571308,\n",
       "       17.20874954, 29.3935327 , 31.75760489, 17.41432104,  7.07033718,\n",
       "       10.56505259, 28.61796752, 28.60862336, 25.89881729, 25.77734322,\n",
       "       21.40427687, 28.99173387, 29.33746775, 17.72267828, 31.7108841 ,\n",
       "       20.30166613, 21.4883743 , 18.36742524, 22.76852407, 24.02998551,\n",
       "       26.43143434, 12.9478131 , 14.7418916 , 27.49666845, 20.92772477,\n",
       "       12.16290376, 27.2817528 , 22.82458902, 20.62871169, 22.20787454,\n",
       "       27.7583049 , 28.50583761, 25.21669369, 29.72057826, 17.79743155,\n",
       "       23.93654393, 19.93724393, 22.31066028, 25.85209649, 22.51623178,\n",
       "       21.9649264 , 24.15145958, 22.37606939, 25.98291472, 23.78703738,\n",
       "       31.97252055, 20.62871169, 24.27293364, 27.46863598, 30.42139018,\n",
       "       28.38436354, 14.00370305, 27.14159042, 30.24385116, 24.29162196,\n",
       "       22.63770584, 13.54583927, 17.17137291, 30.35598106, 22.47885514,\n",
       "       19.02151636, 29.72992242, 24.62801168, 26.36602523, 29.81401985,\n",
       "       25.74931074, 29.27205864, 20.98378972, 30.89794228, 28.25354532,\n",
       "       31.61744251, 31.00072803, 27.87977897, 24.41309603, 28.32829859,\n",
       "        6.65919419, 26.56225257, 15.9005673 ,  9.9950589 , 29.72057826,\n",
       "       18.09644464, 20.82493902, 29.45894181, 19.32052945, 21.23608201,\n",
       "       22.82458902, 18.83463319, 27.7583049 , 28.53387009, 22.09574463,\n",
       "       12.52732595, 26.64635   , 25.07653131, 25.34751191, 12.2656895 ,\n",
       "       24.89899229,  8.68687666, 31.91645559, 19.30184113, 26.04832383,\n",
       "       21.4883743 , 25.49701846, 14.61107338, 31.32777359, 12.91978063,\n",
       "       32.81349485, 25.76799906, 30.14106541, 27.09486962, 29.96352639,\n",
       "       24.50653762, 26.16045373, 22.70311496, 25.60914836, 28.72075326,\n",
       "       22.98343972, 30.37466938, 30.97269555, 16.05007384, 28.37501939,\n",
       "       18.5823409 , 25.1699729 , 25.67455747, 24.17949206, 30.50548761,\n",
       "       19.10561379, 30.40270186, 17.52645094, 26.31930444, 24.06736215,\n",
       "       24.98308972,  9.77079909])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred=model.predict(X_test)# now asking the model to answer from the learned experience\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21.7, 18.5, 22.2, 20.4,  8.8,  8.3, 21.4, 23.6, 23.5, 19. , 10.2,\n",
       "       31.6, 18.4, 19.4, 44. , 23.2, 22. , 33.1, 13.2, 21.7, 16.7, 23.9,\n",
       "       42.3, 18.5, 14.6,  7.5, 33.4, 22. , 28.1, 24.8, 19.9, 24.8, 28.7,\n",
       "       14.1, 43.5, 20.4, 18.3, 13.4, 21. , 22. , 22.6, 13.9, 13.3, 26.5,\n",
       "       20. ,  9.5, 21.6, 22.6, 15.6, 23.1, 27.1, 25.2, 15. , 21.9,  9.6,\n",
       "       22.7, 18.3, 21.4, 20.5, 21.1, 24.5, 31. , 18.7, 18.7, 22.7, 50. ,\n",
       "       16.7, 23. , 20.4, 22.8, 24.8, 10.5, 18.2, 31.5, 23.8, 19.6, 12.7,\n",
       "       15.2, 31.2, 14.5, 17.8, 25. , 23. , 50. , 23.7, 25.1, 24.7, 16.8,\n",
       "       34.7, 23.3, 50. , 46.7, 43.1, 24.4, 24.1,  6.3, 26.4, 16.3, 17.2,\n",
       "       28. , 15.2, 16.6, 20.6, 16.4, 22. , 17.2, 13.5, 23.3, 26.2, 19.7,\n",
       "       13.1, 23.9, 18.2, 19.3,  7. , 18.5, 13.8, 33.4, 12.6, 19.6, 20.8,\n",
       "       25. , 21.7, 43.8, 13.4, 34.9, 24.5, 26.6, 36.5, 31.1, 22.6, 20.3,\n",
       "       18.4, 28.7, 23.1, 17.6, 35.4, 31.6,  8.5, 22.2, 10.4, 22.8, 23.9,\n",
       "       22.2, 32.9, 15.6, 29.8, 14.3, 28.4, 21.4, 23.1, 10.4])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test #actual answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5581683269380144"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train,Y_train) #determinig how good we predicted on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48869790079068526"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,Y_test) #determining how good we predicted on testing data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .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>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>9.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>18.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>5.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>17.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>11.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>18.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>24.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>12.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>14.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>17.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>36.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>12.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>11.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>11.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>17.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>30.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>18.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>7.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>22.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>15.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>18.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>9.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>18.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>17.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>14.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>20.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>6.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>15.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>27.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>7.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>13.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>17.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>28.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>12.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>4.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>10.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>23.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>27.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>10.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>15.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>19.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>19.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>16.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>16.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     LSTAT\n",
       "173   9.04\n",
       "274   3.53\n",
       "491  18.07\n",
       "72    5.52\n",
       "452  17.27\n",
       "76   11.97\n",
       "316  18.33\n",
       "140  24.16\n",
       "471  12.87\n",
       "500  14.33\n",
       "218  17.92\n",
       "9    17.10\n",
       "414  36.98\n",
       "78   12.34\n",
       "323  11.74\n",
       "473  11.66\n",
       "124  17.58\n",
       "388  30.62\n",
       "195   2.97\n",
       "448  18.13\n",
       "271   6.59\n",
       "278   7.19\n",
       "30   22.60\n",
       "501   9.67\n",
       "421  15.70\n",
       "474  18.14\n",
       "79    9.10\n",
       "454  18.71\n",
       "210  17.27\n",
       "497  14.10\n",
       "..     ...\n",
       "418  20.62\n",
       "117  10.30\n",
       "42    5.81\n",
       "322   7.70\n",
       "347   6.36\n",
       "182   4.82\n",
       "155  15.02\n",
       "280   3.76\n",
       "126  27.26\n",
       "329   7.34\n",
       "31   13.04\n",
       "113  17.09\n",
       "148  28.32\n",
       "432  12.03\n",
       "338   8.51\n",
       "57    3.95\n",
       "194   4.38\n",
       "24   16.30\n",
       "17   14.67\n",
       "298   4.97\n",
       "66   10.24\n",
       "211  23.98\n",
       "404  27.38\n",
       "94   10.59\n",
       "154  15.12\n",
       "441  19.52\n",
       "23   19.88\n",
       "225   4.63\n",
       "433  16.22\n",
       "447  16.44\n",
       "\n",
       "[152 rows x 1 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VgtxzDxXbt8PMmfDIIzu/Dj3UqV4ZP95JxSRBZCVTOiubRPJZ3vTAq6qqKCiI/+taaznzzDMxxrheU1ZWRmVlZbvV7NyujVRXVxfz8eOJ7NlHrQRZvJi6bt1g7lx4/32nUmXPPWHNGrjwQqfHftVV8P771NXVdXgwtra2tl3qJ2baR0SSJm8COOApaAaDY6xe5OTJkznjjDMYMmRI3MeLTCUEg220xy8uLqaysjK88sRFaA8+biXIPvvA738PH3wADzzg1JB/9hlMn44dNIiSH/yAHq++CtYmXAaoQUyRzMmbAF5bW8uOHTs8XRtMQ0QTery+vj7m41RWVrarQnHLfRtjGDlyJP3796elpSVuG0Pb4TmIdukC554L//kPvPQSnHMOrcZwwBtvMOH++/np3/7GEStWwBdfeO5BaxBTJHPyJgeeSI8wWAIYb3GqWI/pNkvT7R5rLRUVFcyYMSPu4GZkO9zy+6FBNOpsyX/8g1v33pvKlSs5YsUK9ty8mRGLF1O1ZAmrhg6F734XBg2K2ZYOLeIlIkmRNwHc6yBmMPh42UA5VuB0q/+OF2zjtTFaO+IF0cglBEIHQov23ZfnunXj+eOP5+A33uCo5cvpt2EDxy5bBt/8JowY4Qx6nnyyU7YYIdUbTbtN09f0fZE8WgvFbR2UIUOGUF9f36FA4PaYsXZ2cVv5sLKykjPOOMN9TZSyMiYHdvSJFrzAPYi6PSY4Mz2bmprC0kv9Pv6Y723YQI8nnoBgRc6BB8KllzrT97t18/T6dFasv7PVq1cn9LqL+JkWsyI1iy51pIf42GOPEfk6BAMQEPNNoSNvGrF2CwIoKCigS5cu7WeQbtoEd94Jt9++c62V3Xd3Jgn97Gfhe32mgNsbjzEm6iBw6JucSC5RAE8Cr28A8YLs9OnT22ZsRiorK6O8vDzsU0F5eTlr1651vSd4n1vwitUD93I/zc3OVP2//AVeeGHn8eHDnfTK//wPeCjRTFS8N55o2m1fJ5ID3AJ43uTA3XQ0KLtNqAH3sr5HH32UBQsWxGxPQ0MDq1evDutxL1y4MOo0+8j73ETLkSdyP8XFzrK1Y8c60/T/+leYM8fZ27OmxumJX3opnH++00NPErfxglg9cJF8kjdlhNEkshSql1X3ghNiYlWaeBH6uNEmykQTrXY82J4FCxZQVFQUs77cc/AbOhRmz4YNG5xp+v36QX09TJoEffs6gfzNN709VhxVVVXtJksVFxdzxBFHRD2uyhfJN3F74MaYfsD9wN5AKzDLWvtnY0xPYB4wAFgPjLXWbkldU5MvVlCO7FXHq7VOdFu2eELfVDoisj3B9Eu0FQ8TDX5tn1q+/prukyYxuqCAbyxaBM8+C7fd5nydeqqTXjn99JjplVifgGJVuPTv39+XVSiqnpFk8pJCaQF+Ya19xRjTDVhpjHkKOB+otdbeZIyZAkwBrkpdU5MvkVmE8cr/Elmcyovg43otf4zMj7u1J1pvfsiQIR2uvNm6fTtziosZOXMmFdY66ZV//AOefNL5GjTIGfD88Y+he/eYjxUtLVVRURG1bW7HUykZe556TcOJeBE3gFtrNwIbA99/box5A+gLjAJOCFx2H/AsPgvgXibABMWrtU721PHy8nJPg49BkW1OpD1r1671XEoZ81PL5MkwaxbcdBPcfbfTE3/3XfjFL+C3v+XTM87gsUGDeK+0lLKyMpqamjx/Asq0ZATfRD7xiXiR0CCmMWYAMBRYDuwVCO5YazcaY/Z0uWciMBGgf//+nWlr0iUyizDehBWvPWUviouL29U5x7s+ss2lpaUxq1ZCNTY2tlvHHKIHJk+fWnr2hF/+Ei6/HBYvdqpXamvZY/58fgi8O3Ag/zn6aN7ef/+o6ZVsXEclGcHXy2unFIskwnMAN8bsBjwCTLbWbvO6mp61dhYwC5wywo40MlUSnUUY62N7VVWVa4WJW9VEaWkpLS0t7d5AioqKPAffVPwnD62Y8fpGFXUQtLAQRo2CUaO4/6qrOPjppzl09WoGvfceg957jy3du7PiyCN5ZehQvgpZ2jb4WNkUzGIF33Y7JrmI99opxSKJ8hTAjTHFOMF7jrU2GKU+Nsb0CfS++wCbUtXIVEpWLrWiosI1gFtrKS4ubheohw8fDrR/A4lXahjKrXbb6xuAm+AbTmQQ6ejaJ+/tuivvjRjBkqoqhq5axZH/+Q89t2zhlKee4oRnnuHLXXel9uSTefOII6iqqmpXPtnQ0MCCBQsS2rIumWJ9wvIaZOO9dkqxSKK8VKEYYDbwhrX2lpBTi4AJwE2BP6tT0kIfidXDqqqqilttERS8LhVt6YjQINLRtU+C7fm6tJSXjj2Wl44+mvJ16zj25ZcZWF9P2bZtnLlgASxYwJuPPEL1qFG0RvnUEivVk8oee6xaeq9BNt5rp6V5JVFeeuDHAecBdcaYVYFjV+ME7oeMMRcAHwBjUtNE/4jVw0qkp+9l4g20r/0ODWAlJSVR74ksIywsLMRa63miUEeDZLvfqaCA9YMHUzFlCutee41vjh/fdu2Br7zCga+8wlddunDXhRfyaa9eUR8zNHCmOv0QfAy3T0deg2ysfwcJpadE8FaF8gLglvDWzIkQyVqZL/JxSktL+eqrr8Ly6AUFBW0pGGifP3XbFq5Lly6UlJS4LoQVa5ZjZ4JkrNdmRm0tDVOn0q2hgQn33ccen30GwC5ff82lf/0rANWjRjlL3EYIBrx0pB8qKipcPx0lI8hqad7MyKaxlkTl/VT6ZEtmTj3mnpcR/8i81qE3NjZy5ZVXtj1e5ECl2zouwRRQZ4Kk22sTDIifl5Xx18suw7S2cuqTT3LMSy+1XTOquppR1dWsPfhgFn7ve7QEZmLGW4Y32emHVAbZVC/N21l+DnRu/D5wrADuE/HeGBKpF3f7R/vBBx9QX19Pc3NzW0889D9qrPRBsGa9I/+xI1MHtqCAJ047jSdOO41v1tdzzpw5becGv/46g19/neaiIu695BKOOfPMmNu/JTv9kOogm4kJSl74PdC58fvAsQJ4jvAyaBmvJx26UmSwcsZrrXvo1P/qamc8uzM5/7a12svKmFZeTh/g7HvuYff333fOt7Rw4cyZMHMmtWPGwODBro8dqTM9Sb/2Qjvbbr8HOjd+HzhWAM8R0YJgYWEhJSUl7cruvJYpRv4H9Tq4umPHDtc68mg892qvvRZaW+HXv4bp03f+7vPnUzV/Pm/tvz+PnHUWzSEDuJGP0ZmepF97oclot98DnRu/DxwrgOeIRD7aJ1JiGHpd8LGCtdixuNWRx2q/p2BSUAA33+x81dY6W70FHPD221x94420GsOsiRP56sAD293emZ6kX3uhyWi33wOdG78PHCuA5xCvQTCRyUKR/0GDlRiJTBSKFyw6/PG+qgqs5fXnnqPH+PH0+egjAAqs5eK//925pqAALrus7ZbO9CT92gtNRrv9HujcZPvAcTwK4HkiMkhGzgyNxu0/aEcClts98T7eewnuB3/nO9Q9/jjzlixhaHU133nuuZ0nJ01yvk49FebP71RP0q+90GS02++BLpZsHTj2QgE8D0QLkoWFhVEn9UTLmUfqyCxPt2ARb6MMr7nbtv+El1/uHFi6FL7znZ0XPPkklJUxGbjnpz/lg732ajvltSfp115ostrt50CXqxTA80C0ILljxw5KS0vbTerp0KzKENF69rGCRayP953K3Q4bBtbCZ5/BiBGwbFnbqR/dfjsAT518MmtPP52qk0/2XMcO/uuFemm3X6tr8p0CeB5wC5LBST2JcgsIsHNhp1BuG0bEq99OSs65Z0/497+dYH7jjfCb37SdOmXJEk5ZssQJ9tXV7TaciMavvdBY7fZrdY0ogOeFjuRA4/XIogWEGTNmRO2V19fXR32O0P1EI1VVVblWu8TL3bq2/ZprnK9ly+Bb39p5w9Kl0KOH8/2//w3HHhvz8XONX6trJM83Nc4XbpsDu6U1EtnsOVSiPeZ4Pelo67kUFBTEzN3W1dVRXV3dbmJRWNuPPdbpkW/ZAiecEP4A3/oWGAPXXedckwf8Wl0jCuB5oaKigpEjR4btszly5Miok1yCu9jHGlh049Yz7sjx2tpaduzY0e5cly5dYvYKa2pq2t23Y8cOampq2l/cvTt1M2cy45ZbePKUU8LP/e53TgniMcfAp5+6Pl8uSPTvR7KHUih5Il7uNtoiVpHi9chiVTtES2uUl5eHTd8Pvd6tTj1e/bnb+WjHQ3/nZccdx7LjjqPfxx/z48AgJwDLl0NwOdtnnw2vbMkRfq2uEQVwCfCymmG8Hlm8wc3QQbKFCxcSbVu+4ICn12VbI98YEhHtd/5wr72YceutTL7gAhg7Fh5/fOfJYLrlN7+BadOi7ueZLOmsCvFrdY0ogEuA14Ww4vE6uOm2gcSKFSuor6+nvLy83cbOkW2Itu2am8jNL2Jd39DQAN26QTDt8pe/hM3m5Prrna/DDoMnnoA9o+7n3WGZqArxa3VNvlMOXIDYvWu3nLlXiQ6GNTQ0sHr1aoYMGRIzb19TUxN3JyFov/lFkOfc789/7gxorloFRSF9nlWrYK+9nEHPJUs8/GbexJvcJBKkHrgA7nnQzgTuoI7M3Gxubqa+vt5102aInQ8PPmesdEDCud8hQ6C5Gb74AsaPh0WLdp4LDoL+8pfOQltx0iuxUiSqChGvFMAFSG0eNFqgLCgowBgTtdIkqDMbRXi5tsO/c9euzsQfgFmz4KKLdp774x+dr4MPdnrlffq0uz1eisSva65I+plo+x+mSmVlpY2sOpD8EK3HCbgOVkYT+Ylg2rRpca8NfY6UDs6tXQtHHQVfftnu1MILL2R1375tzx9rgHby5Mmu29ol49OQ+JMxZqW1tjLyuHrgkhZug2QVFRU89thj7coJo4mcHVhaWuqaRmlubqampoaWlpb0DAYOHuykVhob4fzz4aGH2k6NvvNORgPLjz6axdu30+TyqSMY1FUVIl4pgEtG1dXVsXr1as/Xh/Zchw8fHlaFEilacE/5FPHSUpg3D+bN48nx4zn1n/9sO3X08uUcvXw5n/XowX3nn8+2iJRIaIpEVSHihQK4ZJRb/XlwU+VIkUEO4NFHH416rZt0DQYuO+AAlk2dSs9PPuEnd91F6VdfAdBzyxYuv/VWAOaefTZvHXhgQhNntHKgBCmAS0p4DTJuwTS4qXK8CpHgY0bLGRcVFXVoMaxkCQ5GftarF9OnTKGwpYWRixYxZM2atmvOnjsXgE/HjGGPX/0q7mNq5UAJpTpwSbpEFsOKVYvtZf0WcF/rZfjw4Qkt4pVskYuI7Sgq4rEf/IC6NWvgwQfDrt1j/nwoKYF+/WD9etfHVI24hFIPXJIukeVJY9ViJ5IHjnVtptINMQcjKypg3Dh45x04/nj473+dmzZsgIEDne8fegjGjAl7TNWISygFcEm6RIJMqisukjUY2NG8c9zn328/2LgRmprgkktg9uyd58aOdf48/3z4+9+hpEQ14hJGAVySLtEgk+0VF2nJO5eUUDdpErWHHMI+//43Y+fP33nu3nudr969GX7PPTwSZ40YyR8K4JJ0ubY8aTp2rAl9k2gYPJhpgwezx/btTJwzh5KNG52LNm/mgBEjuBp4/JxzWF5envAnFlWw5BYFcEm6XJuIko68c7Q3iU93242/XXklky+9FCZNgr/9re3caXPmcBo4a7JcfLGn58jGCha9oXRO3ABujLkbGAFsstYeEjjWE5gHDADWA2OttVtS10zxm2xPiyQiHXnnmG8SRUVw223O16JFMGrUzgsefND52n13Z/OJAw90fY5s2/syG99Q/MZLGeG94LzZh5gC1Fpry4HawM8iaRXcAm7atGnMmDEj7p6dHZXonqId4Xlp2+9+11nadsMG2H//nce3bYODDgJjeHLcuKivSbZVsKgksvPi9sCttUuNMQMiDo8CTgh8fx/wLHBVEtslElM6e2/pSAklPG7Qty+89Rbs2OEsYTtjRtupU+fO5VTg9YMOYvHWrW2/Q7ZVsGTbG4ofeVqNMBDAF4ekULZaa7uHnN9ire3hcu9EYCJA//79j3j//feT0GzJd8FlZiMFV/Tzo87mgx+96CK+N2tWu1AQhaUAAAsbSURBVOMtxcUUvfIKddZm1SqHufh3mCoZW43QWjsLmAXOcrKpfj7JD6nuvWVicK2z4wZr9tmHNVOn0vXzzznvgQfYa9MmAIqam6Giggqg5zXXML9Xr6wYNMy1aqVM6GgA/9gY08dau9EY0wfYlMxGicSTynSAXwfXgq/JF926cccll0BrK1VPP83xL7zQdk3fG25gMrBt2DB2f+wx2G23tnPpftPKtWqlTOhoAF8ETABuCvxZnbQWiXiQyt5btlVreNXuNSkoYOnw4TRMmULDggWMv/vutmt3X7rU2bgZ4JVXqCsqysibVi5VK2VC3CoUY8w/gWXAAcaYDcaYC3AC9ynGmHrglMDPImnjtoBVMoKBXwfX3F6T+vp66vv3Z9rUqfy/X/2KjyK3eTv8cCoOPZTDn3/eqXAJUEVI9vNShTLO5ZQSVZJRqeq9ZVu1RiKivSYLFixo+/7Lrl2586KLwFpOePZZvvPcc23nTnv8cU57/HHeGTSIh8aOpWmXXbL+TSvfaTlZkQjpqPtOp6hvPMbw6ujRTo976dKwU/u9+y6/vukmrp06lf0VwLOaArhIhFSmZzIh7hvSt79N3Zo13HLNNXzQr1/YdeNuvRWMgenTw9Irkh20K71IFklVJYiXx227ZutWTn75ZY773/9t/0DDhkF1NXTv3v6ctJOsv0+3OnAFcJEsEVm+CJmdaAPAsmXwrW9FP/fii+7nJKl/n24BXCkUkSyRlWuDHHuskzrZuhVOPDH83HHHOemV665TeiWKdPx9KoCLZImsLl8sK4Onn4bWVvjjH8PP/e53UFAAxxwDn36amfZloXT8fSqAi2QJzysSZpIx8ItfOD3ul18OP7d8OfTq5VwTUp6Yr9Lx96kALpIlfFe+WFnpBPJt2+C0iBWnTzjBCeS//a3Ta89D6fj7VAAXyRK+LV/s1g1qapxgPnNm+Lnrr4fCQhg6FDbl15JJ6fj7VBWKiCTf6tVOD72lpf25p56Ck09Of5t8LGPLyYpIHhoyBJqb4YsvnH07Fy3aee6UUwDYPGECcw49lIbPP9dKhB2kHriIpMesWXDRRe0Ob+rdmwd++EO+7tnTHymjDNBEHhHJDmvX0nz44RQ3NbU7tfDCCxkdZVehfKeJPCKSHQYP5sarr+aGa67htcGDw06NvvNOp3pl0iRnv0+JSQFcRNKurKyMluJiHhkzhmlTp7Jw1KjwC2bOhKIi2G8/+PDDzDTSBxTARSTtImukVw8dyo033MBbixZBj5D90d99F/r3d3rl1dr4K5ICuIiknVuN9AEjR8Jnn8FXX8G554bfNHq0E8gvvtipcBENYopIlvvnP51SxEj77gvPPw8DBqS9SemmQUwR8adx45xZnuvWwd577zy+YQMMHOj0yh96KHPtyyAFcBHxh/32g40boakJfvKT8HM/+IETyH/0I+d8nlAAFxF/KS6GO+90euUPPxx+7t57oUsX6N3b6bHnOAVwEfGv73/fCeTr14fnwj/5BMrLnV75P/6RqdalnAK4iPjfN74B773nVKdcckn4ufPOcwL5uHFOdUsOUQAXkdxRVAS33eb0ykMX0AKYOxdKS53dhd58MzPtSzIFcBHJTSNHOoF8wwbYf/+dx7dtg4MOcnrls2dnrn1JoAAuIrmtb1946y1nbfIrrgg/95OfOIH8+9+HL7/MTPs6QQFcRPJDYSH86U9Or7ymJvzcggXQtatTwVJXl5n2dYACuIjkn9NOcwL5xo0Quv54UxMceqjTK7/99sy1zyMFcBHJX3vvDWvWOEvXTpkSfu6SS5xAPmIEbN+emfbF0akAbow5zRjzljFmnTFmSvw7RESyUEEB/OEPTq+8tjb83GOPORs3GwOvvpqZ9rnocAA3xhQCtwHDgYOBccaYg5PVMBGRjDjpJCeQb9rkbMwc6vDDnUA+Y4ZzTYZ1pgd+FLDOWvuutbYJmAuMinOPiIg/9O4NL78Mra1w7bXh5y6/3Om1n3KKU5aYIZ0J4H2B0K0yNgSOhTHGTDTGrDDGrNi8eXMnnk5EJAOMgalTnR730qXh55YscSYGGeME+zTrTAA3UY61+0xhrZ1lra201lb27t27E08nIpJh3/62E8g//RSOOy783FFHOYH85pvTll7pTADfAPQL+Xlf4KPONUdExAd69oQXXnDSKzfeGH5uyhQnvTJsGGzZktJmdCaAvwyUG2MGGmNKgLOBRXHuERHJHcbAr3/t9LiXLQs/9/zzTqA3Bl58MSVP3+EAbq1tAS4FngDeAB6y1q5NVsNERHzlmGOcQL51q1PJEur442Hx4qQ/ZafqwK21/2ut3d9au5+19oZkNUpExLfKypxa8tZWZ+p+0J57Jv2pNBNTRCQVjHEWz7LW+TrqqKQ/hQK4iIhPKYCLiPiUAriIiE8pgIuI+JQCuIiITymAi4j4lAK4iIhPKYCLiPiUsWlclNwYsxl4P21P6K4X8EmmG5Egv7XZb+0F/7XZb+0FtbmjvmGtbbeca1oDeLYwxqyw1lbGvzJ7+K3Nfmsv+K/NfmsvqM3JphSKiIhPKYCLiPhUvgbwWZluQAf4rc1+ay/4r81+ay+ozUmVlzlwEZFckK89cBER31MAFxHxqbwL4MaY9caYOmPMKmPMiky3J5Ix5m5jzCZjzGshx3oaY54yxtQH/uyRyTZGcmnzVGPM/wVe51XGmNMz2cZQxph+xphnjDFvGGPWGmMmBY5n7esco83Z/DrvYoz5jzFmdaDN0wLHBxpjlgde53mBPXUzLkZ77zXGvBfyGh+W6bYG5V0O3BizHqi01ma6MD8qY8wwYDtwv7X2kMCx6cBn1tqbjDFTgB7W2qsy2c5QLm2eCmy31v4xk22LxhjTB+hjrX3FGNMNWAmMBs4nS1/nGG0eS/a+zgboaq3dbowpBl4AJgFXAAustXONMXcAq621t2eyrRCzvRcDi621D2e0gVHkXQ8821lrlwKfRRweBdwX+P4+nP+4WcOlzVnLWrvRWvtK4PvPcTbl7ksWv84x2py1rGN74MfiwJcFTgKCwTBrXucY7c1a+RjALfCkMWalMWZiphvj0V7W2o3g/EcGkr87ampcaoxZE0ixZE06IpQxZgAwFFiOT17niDZDFr/OxphCY8wqYBPwFPAOsNVa2xK4ZANZ9EYU2V5rbfA1viHwGt9qjOmSwSaGyccAfpy19nBgOPCzwMd/Sb7bgf2Aw4CNwJ9iX55+xpjdgEeAydbabZlujxdR2pzVr7O1doe19jBgX+Ao4KBol6W3Ve4i22uMOQT4NXAgcCTQE8iKtBrkYQC31n4U+HMT8CjOP6ps93EgBxrMhW7KcHvistZ+HPjP0ArcSZa9zoEc5yPAHGvtgsDhrH6do7U521/nIGvtVuBZ4BiguzGmKHBqX+CjTLXLTUh7Twukr6y19mvgHrLoNc6rAG6M6RoYAMIY0xU4FXgt9l1ZYREwIfD9BKA6g23xJBgIA75HFr3OgcGq2cAb1tpbQk5l7evs1uYsf517G2O6B74vBU7Gyd0/A5wVuCxrXmeX9r4Z8qZucPL12fMa51MVijFmEE6vG6AIeNBae0MGm9SOMeafwAk4S1h+DFwLLAQeAvoDHwBjrLVZM2jo0uYTcD7WW2A9cFEwv5xpxpjjgeeBOqA1cPhqnJxyVr7OMdo8jux9nQ/FGaQsxOksPmSt/X3g/+FcnHTEq8C5gd5tRsVo79NAb8AAq4CLQwY7MyqvAriISC7JqxSKiEguUQAXEfEpBXAREZ9SABcR8SkFcBERn1IAFxHxKQVwERGf+v+G96OJzbxKVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test['LSTAT'], Y_test,  color='gray') #plotting the points in form of dots through scatter function\n",
    "plt.plot(X_test['LSTAT'], Y_pred, color='red', linewidth=2)# determinig the best fit line\n",
    "plt.show()# function to display after pllot creaion on screen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.98620321])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_ # determining coeffiecents(a1,a2,....)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.26557743777449"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_# determining intercepts(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Regression\n",
    "   \n",
    "   using all the features to determine how the end target is depended on all attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# similar step as explained earlier\n",
    "model=LinearRegression()\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([28.64896005, 36.49501384, 15.4111932 , 25.40321303, 18.85527988,\n",
       "       23.14668944, 17.3921241 , 14.07859899, 23.03692679, 20.59943345,\n",
       "       24.82286159, 18.53057049, -6.86543527, 21.80172334, 19.22571177,\n",
       "       26.19191985, 20.27733882,  5.61596432, 40.44887974, 17.57695918,\n",
       "       27.44319095, 30.1715964 , 10.94055823, 24.02083139, 18.07693812,\n",
       "       15.934748  , 23.12614028, 14.56052142, 22.33482544, 19.3257627 ,\n",
       "       22.16564973, 25.19476081, 25.31372473, 18.51345025, 16.6223286 ,\n",
       "       17.50268505, 30.94992991, 20.19201752, 23.90440431, 24.86975466,\n",
       "       13.93767876, 31.82504715, 42.56978796, 17.62323805, 27.01963242,\n",
       "       17.19006621, 13.80594006, 26.10356557, 20.31516118, 30.08649576,\n",
       "       21.3124053 , 34.15739602, 15.60444981, 26.11247588, 39.31613646,\n",
       "       22.99282065, 18.95764781, 33.05555669, 24.85114223, 12.91729352,\n",
       "       22.68101452, 30.80336295, 31.63522027, 16.29833689, 21.07379993,\n",
       "       16.57699669, 20.36362023, 26.15615896, 31.06833034, 11.98679953,\n",
       "       20.42550472, 27.55676301, 10.94316981, 16.82660609, 23.92909733,\n",
       "        5.28065815, 21.43504661, 41.33684993, 18.22211675,  9.48269245,\n",
       "       21.19857446, 12.95001331, 21.64822797,  9.3845568 , 23.06060014,\n",
       "       31.95762512, 19.16662892, 25.59942257, 29.35043558, 20.13138581,\n",
       "       25.57297369,  5.42970803, 20.23169356, 15.1949595 , 14.03241742,\n",
       "       20.91078077, 24.82249135, -0.47712079, 13.70520524, 15.69525576,\n",
       "       22.06972676, 24.64152943, 10.7382866 , 19.68622564, 23.63678009,\n",
       "       12.07974981, 18.47894211, 25.52713393, 20.93461307, 24.6955941 ,\n",
       "        7.59054562, 19.01046053, 21.9444339 , 27.22319977, 32.18608828,\n",
       "       15.27826455, 34.39190421, 12.96314168, 21.01681316, 28.57880911,\n",
       "       15.86300844, 24.85124135,  3.37937111, 23.90465773, 25.81792146,\n",
       "       23.11020547, 25.33489201, 33.35545176, 20.60724498, 38.4772665 ,\n",
       "       13.97398533, 25.21923987, 17.80946626, 20.63437371,  9.80267398,\n",
       "       21.07953576, 22.3378417 , 32.32381854, 31.48694863, 15.46621287,\n",
       "       16.86242766, 28.99330526, 24.95467894, 16.73633557,  6.12858395,\n",
       "       26.65990044, 23.34007187, 17.40367164, 13.38594123, 39.98342478,\n",
       "       16.68286302, 18.28561759])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred=model.predict(X_test)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7434997532004697"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.33470103e-01,  3.58089136e-02,  4.95226452e-02,  3.11983512e+00,\n",
       "       -1.54170609e+01,  4.05719923e+00, -1.08208352e-02, -1.38599824e+00,\n",
       "        2.42727340e-01, -8.70223437e-03, -9.10685208e-01,  1.17941159e-02,\n",
       "       -5.47113313e-01])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.631084035694585"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot to show how much importance eaach feature has in determinig the target\n",
    "plt.bar(boston.feature_names,model.coef_)\n",
    "plt.xticks(rotation= 60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}