boston_data_with_polynomial_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a60fcf32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "084f8a54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
       "         4.9800e+00],\n",
       "        [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
       "         9.1400e+00],\n",
       "        [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
       "         4.0300e+00],\n",
       "        ...,\n",
       "        [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "         5.6400e+00],\n",
       "        [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
       "         6.4800e+00],\n",
       "        [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "         7.8800e+00]]),\n",
       " 'target': 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]),\n",
       " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "        'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),\n",
       " 'DESCR': \".. _boston_dataset:\\n\\nBoston 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\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\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",
       " 'filename': 'C:\\\\Users\\\\Admin\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\boston_house_prices.csv'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_dataset=datasets.load_boston()\n",
    "boston_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "39f230cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=boston_dataset.data\n",
    "y=boston_dataset.target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cdf4972c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
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       "      <th>LSTAT</th>\n",
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       "      <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",
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       "    <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",
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       "      <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",
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       "    <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",
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       "    <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",
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      "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",
       "\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  "
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(x,columns=boston_dataset.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "599ab0a6",
   "metadata": {},
   "outputs": [
    {
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       "    <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11ba0acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f0409da",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation=df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c897c2f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(correlation,cbar=True,square=True,fmt='.1f',annot=True,annot_kws={'size':18},cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b8b7f5f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "53cec658",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "189c6ae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c7d8ecb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bcc2c76a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred=model.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "df038f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7433062334946119"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score=metrics.r2_score(y_train,y_train_pred)\n",
    "train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b4fb06c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7219820791210037"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score=metrics.r2_score(y_test,y_test_pred)\n",
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "72f0caee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x27a5ac4d430>"
      ]
     },
     "execution_count": 27,
     "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.scatter(y_test,y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "91268553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.41978194,  0.28482986, -1.2879095 , ..., -0.29400884,\n",
       "        -0.64349494,  0.19452681],\n",
       "       [-0.41733926, -0.48772236, -0.59338101, ..., -0.43546358,\n",
       "        -0.13368026,  0.19452681],\n",
       "       [-0.41734159, -0.48772236, -0.59338101, ..., -0.39140406,\n",
       "        -0.1201547 ,  0.15715436],\n",
       "       ...,\n",
       "       [-0.41344658, -0.48772236,  0.11573841, ..., -0.35425808,\n",
       "         0.51888253,  0.19452681],\n",
       "       [-0.40776407, -0.48772236,  0.11573841, ..., -0.32387496,\n",
       "         0.47438031,  0.16259032],\n",
       "       [-0.41500016, -0.48772236,  0.11573841, ..., -0.35425808,\n",
       "         0.51888253,  0.19452681]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(1,13):\n",
    "    for j in range(1,13):\n",
    "        \n",
    "        c=x[:,i-1]*x[:,j-1]\n",
    "        x=np.c_[x,c]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7f603528",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "      <th>300</th>\n",
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       "      <td>-0.419782</td>\n",
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       "      <td>-0.982843</td>\n",
       "      <td>-0.666608</td>\n",
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       "      <td>-0.568035</td>\n",
       "      <td>-0.120230</td>\n",
       "      <td>-0.063607</td>\n",
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       "      <td>-0.052932</td>\n",
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       "      <td>-0.433485</td>\n",
       "      <td>-0.294009</td>\n",
       "      <td>-0.643495</td>\n",
       "      <td>0.194527</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.417339</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>-0.593381</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>-0.740262</td>\n",
       "      <td>0.194274</td>\n",
       "      <td>0.367166</td>\n",
       "      <td>0.557160</td>\n",
       "      <td>-0.867883</td>\n",
       "      <td>-0.987329</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.261712</td>\n",
       "      <td>-0.120230</td>\n",
       "      <td>-0.326494</td>\n",
       "      <td>0.085685</td>\n",
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       "      <td>0.245736</td>\n",
       "      <td>-0.382781</td>\n",
       "      <td>-0.435464</td>\n",
       "      <td>-0.133680</td>\n",
       "      <td>0.194527</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.417342</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>-0.593381</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>-0.740262</td>\n",
       "      <td>1.282714</td>\n",
       "      <td>-0.265812</td>\n",
       "      <td>0.557160</td>\n",
       "      <td>-0.867883</td>\n",
       "      <td>-0.987329</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.235232</td>\n",
       "      <td>-0.108065</td>\n",
       "      <td>-0.293460</td>\n",
       "      <td>0.508502</td>\n",
       "      <td>-0.105375</td>\n",
       "      <td>0.220873</td>\n",
       "      <td>-0.344052</td>\n",
       "      <td>-0.391404</td>\n",
       "      <td>-0.120155</td>\n",
       "      <td>0.157154</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.416750</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>-1.306878</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>-0.835284</td>\n",
       "      <td>1.016303</td>\n",
       "      <td>-0.809889</td>\n",
       "      <td>1.077737</td>\n",
       "      <td>-0.752922</td>\n",
       "      <td>-1.106115</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.543874</td>\n",
       "      <td>-0.113445</td>\n",
       "      <td>-0.347614</td>\n",
       "      <td>0.422947</td>\n",
       "      <td>-0.337046</td>\n",
       "      <td>0.448514</td>\n",
       "      <td>-0.313338</td>\n",
       "      <td>-0.460324</td>\n",
       "      <td>0.047040</td>\n",
       "      <td>0.173192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.412482</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>-1.306878</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>-0.835284</td>\n",
       "      <td>1.228577</td>\n",
       "      <td>-0.511180</td>\n",
       "      <td>1.077737</td>\n",
       "      <td>-0.752922</td>\n",
       "      <td>-1.106115</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.576401</td>\n",
       "      <td>-0.120230</td>\n",
       "      <td>-0.368404</td>\n",
       "      <td>0.541866</td>\n",
       "      <td>-0.225457</td>\n",
       "      <td>0.475338</td>\n",
       "      <td>-0.332078</td>\n",
       "      <td>-0.487854</td>\n",
       "      <td>0.049853</td>\n",
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       "        0         1         2         3         4         5         6    \\\n",
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       "1 -0.417339 -0.487722 -0.593381 -0.272599 -0.740262  0.194274  0.367166   \n",
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       "4 -0.412482 -0.487722 -1.306878 -0.272599 -0.835284  1.228577 -0.511180   \n",
       "\n",
       "        7         8         9    ...       291       292       293       294  \\\n",
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       "2  0.557160 -0.867883 -0.987329  ... -0.235232 -0.108065 -0.293460  0.508502   \n",
       "3  1.077737 -0.752922 -1.106115  ... -0.543874 -0.113445 -0.347614  0.422947   \n",
       "4  1.077737 -0.752922 -1.106115  ... -0.576401 -0.120230 -0.368404  0.541866   \n",
       "\n",
       "        295       296       297       298       299       300  \n",
       "0 -0.052932  0.061841 -0.433485 -0.294009 -0.643495  0.194527  \n",
       "1  0.161939  0.245736 -0.382781 -0.435464 -0.133680  0.194527  \n",
       "2 -0.105375  0.220873 -0.344052 -0.391404 -0.120155  0.157154  \n",
       "3 -0.337046  0.448514 -0.313338 -0.460324  0.047040  0.173192  \n",
       "4 -0.225457  0.475338 -0.332078 -0.487854  0.049853  0.194527  \n",
       "\n",
       "[5 rows x 301 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.DataFrame(x)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "032a047f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <th>299</th>\n",
       "      <th>300</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.060000e+02</td>\n",
       "      <td>5.060000e+02</td>\n",
       "      <td>5.060000e+02</td>\n",
       "      <td>5.060000e+02</td>\n",
       "      <td>5.060000e+02</td>\n",
       "      <td>5.060000e+02</td>\n",
       "      <td>5.060000e+02</td>\n",
       "      <td>5.060000e+02</td>\n",
       "      <td>5.060000e+02</td>\n",
       "      <td>5.060000e+02</td>\n",
       "      <td>...</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-8.787437e-17</td>\n",
       "      <td>-6.343191e-16</td>\n",
       "      <td>-2.682911e-15</td>\n",
       "      <td>4.701992e-16</td>\n",
       "      <td>2.490322e-15</td>\n",
       "      <td>-1.145230e-14</td>\n",
       "      <td>-1.407855e-15</td>\n",
       "      <td>9.210902e-16</td>\n",
       "      <td>5.441409e-16</td>\n",
       "      <td>-8.868619e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000990e+00</td>\n",
       "      <td>1.000990e+00</td>\n",
       "      <td>1.000990e+00</td>\n",
       "      <td>1.000990e+00</td>\n",
       "      <td>1.000990e+00</td>\n",
       "      <td>1.000990e+00</td>\n",
       "      <td>1.000990e+00</td>\n",
       "      <td>1.000990e+00</td>\n",
       "      <td>1.000990e+00</td>\n",
       "      <td>1.000990e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.950919</td>\n",
       "      <td>0.640672</td>\n",
       "      <td>1.148966</td>\n",
       "      <td>0.881274</td>\n",
       "      <td>0.803905</td>\n",
       "      <td>0.823446</td>\n",
       "      <td>1.451312</td>\n",
       "      <td>1.328859</td>\n",
       "      <td>0.904964</td>\n",
       "      <td>3.026859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.197819e-01</td>\n",
       "      <td>-4.877224e-01</td>\n",
       "      <td>-1.557842e+00</td>\n",
       "      <td>-2.725986e-01</td>\n",
       "      <td>-1.465882e+00</td>\n",
       "      <td>-3.880249e+00</td>\n",
       "      <td>-2.335437e+00</td>\n",
       "      <td>-1.267069e+00</td>\n",
       "      <td>-9.828429e-01</td>\n",
       "      <td>-1.313990e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.969705</td>\n",
       "      <td>-10.806105</td>\n",
       "      <td>-8.048751</td>\n",
       "      <td>-2.603451</td>\n",
       "      <td>-4.338331</td>\n",
       "      <td>-0.557292</td>\n",
       "      <td>-6.490806</td>\n",
       "      <td>-5.981626</td>\n",
       "      <td>-3.542620</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-4.109696e-01</td>\n",
       "      <td>-4.877224e-01</td>\n",
       "      <td>-8.676906e-01</td>\n",
       "      <td>-2.725986e-01</td>\n",
       "      <td>-9.130288e-01</td>\n",
       "      <td>-5.686303e-01</td>\n",
       "      <td>-8.374480e-01</td>\n",
       "      <td>-8.056878e-01</td>\n",
       "      <td>-6.379618e-01</td>\n",
       "      <td>-7.675760e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.414388</td>\n",
       "      <td>-0.118347</td>\n",
       "      <td>-0.402336</td>\n",
       "      <td>-0.165728</td>\n",
       "      <td>-0.465234</td>\n",
       "      <td>-0.190343</td>\n",
       "      <td>-0.281374</td>\n",
       "      <td>-0.346399</td>\n",
       "      <td>-0.343384</td>\n",
       "      <td>0.108636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-3.906665e-01</td>\n",
       "      <td>-4.877224e-01</td>\n",
       "      <td>-2.110985e-01</td>\n",
       "      <td>-2.725986e-01</td>\n",
       "      <td>-1.442174e-01</td>\n",
       "      <td>-1.084655e-01</td>\n",
       "      <td>3.173816e-01</td>\n",
       "      <td>-2.793234e-01</td>\n",
       "      <td>-5.230014e-01</td>\n",
       "      <td>-4.646726e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.204658</td>\n",
       "      <td>-0.102431</td>\n",
       "      <td>-0.156488</td>\n",
       "      <td>0.015095</td>\n",
       "      <td>-0.093164</td>\n",
       "      <td>0.055781</td>\n",
       "      <td>-0.207103</td>\n",
       "      <td>-0.200284</td>\n",
       "      <td>-0.011053</td>\n",
       "      <td>0.167804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.396560e-03</td>\n",
       "      <td>4.877224e-02</td>\n",
       "      <td>1.015999e+00</td>\n",
       "      <td>-2.725986e-01</td>\n",
       "      <td>5.986790e-01</td>\n",
       "      <td>4.827678e-01</td>\n",
       "      <td>9.067981e-01</td>\n",
       "      <td>6.623709e-01</td>\n",
       "      <td>1.661245e+00</td>\n",
       "      <td>1.530926e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088952</td>\n",
       "      <td>-0.036482</td>\n",
       "      <td>0.055826</td>\n",
       "      <td>0.288763</td>\n",
       "      <td>0.213373</td>\n",
       "      <td>0.427421</td>\n",
       "      <td>-0.057312</td>\n",
       "      <td>0.002820</td>\n",
       "      <td>0.253779</td>\n",
       "      <td>0.194527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.933931e+00</td>\n",
       "      <td>3.804234e+00</td>\n",
       "      <td>2.422565e+00</td>\n",
       "      <td>3.668398e+00</td>\n",
       "      <td>2.732346e+00</td>\n",
       "      <td>3.555044e+00</td>\n",
       "      <td>1.117494e+00</td>\n",
       "      <td>3.960518e+00</td>\n",
       "      <td>1.661245e+00</td>\n",
       "      <td>1.798194e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.178469</td>\n",
       "      <td>1.617954</td>\n",
       "      <td>1.205107</td>\n",
       "      <td>8.520698</td>\n",
       "      <td>1.148907</td>\n",
       "      <td>4.396001</td>\n",
       "      <td>1.639309</td>\n",
       "      <td>0.817971</td>\n",
       "      <td>5.115016</td>\n",
       "      <td>15.266160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 301 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0             1             2             3             4    \\\n",
       "count  5.060000e+02  5.060000e+02  5.060000e+02  5.060000e+02  5.060000e+02   \n",
       "mean  -8.787437e-17 -6.343191e-16 -2.682911e-15  4.701992e-16  2.490322e-15   \n",
       "std    1.000990e+00  1.000990e+00  1.000990e+00  1.000990e+00  1.000990e+00   \n",
       "min   -4.197819e-01 -4.877224e-01 -1.557842e+00 -2.725986e-01 -1.465882e+00   \n",
       "25%   -4.109696e-01 -4.877224e-01 -8.676906e-01 -2.725986e-01 -9.130288e-01   \n",
       "50%   -3.906665e-01 -4.877224e-01 -2.110985e-01 -2.725986e-01 -1.442174e-01   \n",
       "75%    7.396560e-03  4.877224e-02  1.015999e+00 -2.725986e-01  5.986790e-01   \n",
       "max    9.933931e+00  3.804234e+00  2.422565e+00  3.668398e+00  2.732346e+00   \n",
       "\n",
       "                5             6             7             8             9    \\\n",
       "count  5.060000e+02  5.060000e+02  5.060000e+02  5.060000e+02  5.060000e+02   \n",
       "mean  -1.145230e-14 -1.407855e-15  9.210902e-16  5.441409e-16 -8.868619e-16   \n",
       "std    1.000990e+00  1.000990e+00  1.000990e+00  1.000990e+00  1.000990e+00   \n",
       "min   -3.880249e+00 -2.335437e+00 -1.267069e+00 -9.828429e-01 -1.313990e+00   \n",
       "25%   -5.686303e-01 -8.374480e-01 -8.056878e-01 -6.379618e-01 -7.675760e-01   \n",
       "50%   -1.084655e-01  3.173816e-01 -2.793234e-01 -5.230014e-01 -4.646726e-01   \n",
       "75%    4.827678e-01  9.067981e-01  6.623709e-01  1.661245e+00  1.530926e+00   \n",
       "max    3.555044e+00  1.117494e+00  3.960518e+00  1.661245e+00  1.798194e+00   \n",
       "\n",
       "       ...         291         292         293         294         295  \\\n",
       "count  ...  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean   ...   -0.356977    0.048788   -0.380051    0.128069   -0.273534   \n",
       "std    ...    0.950919    0.640672    1.148966    0.881274    0.803905   \n",
       "min    ...   -3.969705  -10.806105   -8.048751   -2.603451   -4.338331   \n",
       "25%    ...   -0.414388   -0.118347   -0.402336   -0.165728   -0.465234   \n",
       "50%    ...   -0.204658   -0.102431   -0.156488    0.015095   -0.093164   \n",
       "75%    ...    0.088952   -0.036482    0.055826    0.288763    0.213373   \n",
       "max    ...    1.178469    1.617954    1.205107    8.520698    1.148907   \n",
       "\n",
       "              296         297         298         299         300  \n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  \n",
       "mean     0.291512   -0.444413   -0.441808   -0.177383    1.000000  \n",
       "std      0.823446    1.451312    1.328859    0.904964    3.026859  \n",
       "min     -0.557292   -6.490806   -5.981626   -3.542620    0.000012  \n",
       "25%     -0.190343   -0.281374   -0.346399   -0.343384    0.108636  \n",
       "50%      0.055781   -0.207103   -0.200284   -0.011053    0.167804  \n",
       "75%      0.427421   -0.057312    0.002820    0.253779    0.194527  \n",
       "max      4.396001    1.639309    0.817971    5.115016   15.266160  \n",
       "\n",
       "[8 rows x 301 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "67d1b06a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "14ee3e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8a8fdbd3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "4a3210bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred2_train=model2.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "7eb015ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred2=model2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "157cbc7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88790447142288"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score2_train=metrics.r2_score(y_train,y_pred2_train)\n",
    "score2_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e3b04c7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8770562845274321"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score2=metrics.r2_score(y_test,y_pred2)\n",
    "score2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a4691218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x27a5bd3f2b0>"
      ]
     },
     "execution_count": 109,
     "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.scatter(y_test,y_pred2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}