3a.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21a5e66d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac787c00",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</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</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</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</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>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</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</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</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
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       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
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       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
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       "      <td>11.93</td>\n",
       "      <td>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</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 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  0.538  6.575  65.2  4.0900    1  296   \n",
       "1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
       "2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242   \n",
       "3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
       "4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...  ...   \n",
       "501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273   \n",
       "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273   \n",
       "503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273   \n",
       "504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273   \n",
       "505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV  \n",
       "0       15.3  396.90   4.98  24.0  \n",
       "1       17.8  396.90   9.14  21.6  \n",
       "2       17.8  392.83   4.03  34.7  \n",
       "3       18.7  394.63   2.94  33.4  \n",
       "4       18.7  396.90   5.33  36.2  \n",
       "..       ...     ...    ...   ...  \n",
       "501     21.0  391.99   9.67  22.4  \n",
       "502     21.0  396.90   9.08  20.6  \n",
       "503     21.0  396.90   5.64  23.9  \n",
       "504     21.0  393.45   6.48  22.0  \n",
       "505     21.0  396.90   7.88  11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('dataset/boston_housing.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "31fb5728",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>6.575</td>\n",
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       "      <td>7.185</td>\n",
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       "<p>506 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "        RM\n",
       "0    6.575\n",
       "1    6.421\n",
       "2    7.185\n",
       "3    6.998\n",
       "4    7.147\n",
       "..     ...\n",
       "501  6.593\n",
       "502  6.120\n",
       "503  6.976\n",
       "504  6.794\n",
       "505  6.030\n",
       "\n",
       "[506 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df[['RM']]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9b723db5",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.6</td>\n",
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       "      <th>2</th>\n",
       "      <td>34.7</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.4</td>\n",
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       "      <th>4</th>\n",
       "      <td>36.2</td>\n",
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       "      <td>22.4</td>\n",
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      "text/plain": [
       "     MEDV\n",
       "0    24.0\n",
       "1    21.6\n",
       "2    34.7\n",
       "3    33.4\n",
       "4    36.2\n",
       "..    ...\n",
       "501  22.4\n",
       "502  20.6\n",
       "503  23.9\n",
       "504  22.0\n",
       "505  11.9\n",
       "\n",
       "[506 rows x 1 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df[['MEDV']]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d8830113",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4031293e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12ed2d78bb0>"
      ]
     },
     "execution_count": 12,
     "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(df['RM'] , df['MEDV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f4daa5fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM\n",
       "0    0.00632\n",
       "1    0.02731\n",
       "2    0.02729\n",
       "3    0.03237\n",
       "4    0.06905\n",
       "..       ...\n",
       "501  0.06263\n",
       "502  0.04527\n",
       "503  0.06076\n",
       "504  0.10959\n",
       "505  0.04741\n",
       "\n",
       "[506 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,:1]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "53085509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      24.0\n",
       "1      21.6\n",
       "2      34.7\n",
       "3      33.4\n",
       "4      36.2\n",
       "       ... \n",
       "501    22.4\n",
       "502    20.6\n",
       "503    23.9\n",
       "504    22.0\n",
       "505    11.9\n",
       "Name: MEDV, Length: 506, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,-1]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "34433ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.67,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b99b530e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(339, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2dc841ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "db423c5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(339,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "861b22ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "67219448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr=LinearRegression()\n",
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "729a5e57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21.54917784, 23.31485649, 23.70891534, 23.70093426, 16.18442689,\n",
       "       23.12602628, 23.69596584, 23.72767198,  4.50584691, 23.11551022,\n",
       "       23.71351141, 23.72241395, 23.71680238, 22.74734285, 17.98859297,\n",
       "       23.72830385, 23.58523129, 20.20665773, 16.9367614 , 20.0057318 ,\n",
       "       23.72451641, 23.65898662, 23.53168815, 22.34955674, 19.56848415,\n",
       "       23.43666751, 23.18818988, 23.72440358, 23.73364462, 23.72553944,\n",
       "       19.20711685, 23.72367769, 23.73034989, 23.72509562, 14.4103873 ,\n",
       "       22.92803778, 20.28323766, 22.29066155, 23.71216493, 23.70582747,\n",
       "       23.7252724 , 23.26776365, 23.71863404, 14.56380291, 21.54810216,\n",
       "       20.48718752, 23.70598919, 21.60739228, 23.49160226, 16.66355449,\n",
       "       23.71359415, 23.44306515,  9.31404785, 23.72464053, 19.24363722,\n",
       "       23.44784552, 23.72713791, 23.67169165, 23.45639452, 23.65688792,\n",
       "       23.68553253, 23.69848578, 23.64634554, 23.72002565, 23.65944924,\n",
       "       23.6826628 , 23.72537395, 23.50404776, 20.67261768, 23.73445326,\n",
       "       23.21653736, 15.23956125, 23.7215489 , 18.82088864, 23.54277966,\n",
       "       23.69735745, 22.86829257, 23.53660392, 23.72210554, 23.71954799,\n",
       "       23.72594187, 23.63942134, 20.79012223, 23.69064387, 23.59484844,\n",
       "       23.48902214, 18.34751554, 23.70405598, 23.72196262, 22.54871119,\n",
       "       23.69037307, 23.49863177, 17.76804157, 23.73279085, 23.53528753,\n",
       "       17.77334473, 23.62642295, 23.73188819, 23.70330376, 23.40712776,\n",
       "       23.7331632 , 23.67553926, 23.67027747, 23.70843391, 23.73118486,\n",
       "       23.72831137, 23.42243923, 20.65424843, 23.71262755, 23.68183536,\n",
       "        9.57352672, 21.78213337, 20.70870552, 14.31846581, 23.69126445,\n",
       "       20.26620736, 23.72672795, 22.84396948, 23.71984136, 23.68179022,\n",
       "       23.73368223, 23.28989778, 23.25961333, 18.5940938 , 23.7155236 ,\n",
       "       23.70773811, 23.68420862, 23.64015099, 23.71368818, 23.65805387,\n",
       "       23.6872664 , 23.56445116, 23.6850511 , 18.03026605, 23.68759737,\n",
       "       23.62967254, 20.04385439, 15.44491777, 18.71380988, 22.63882735,\n",
       "       23.62965374, 21.37356417, 23.70537989, 21.58302405, 20.82287773,\n",
       "       23.20681113, 23.37142732, 23.31326554, 23.73293001, 23.65399187,\n",
       "       23.73293001, 23.44362932, 22.35686834, 23.70245375, 22.40901987,\n",
       "       23.71521519, 23.68412963, 23.71544086, 23.72142478, 23.71683623,\n",
       "       23.7321477 , 23.70096435, 23.36154313, 23.5210555 , 22.48949254,\n",
       "       -1.80717348, 23.72918019])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=lr.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fe513ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.73857543674516"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9b2579b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.37611084])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3871bff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "48437f9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.28057994564443"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cfd33264",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "af343381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.837738334489003"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f751a42a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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