lab1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "boston = datasets.load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(boston.data)\n",
    "df.columns = boston.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
       "      <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",
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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",
       "    </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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX   \n",
       "0  0.00632  18.0   2.31   0.0  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  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Price'] = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Price</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",
       "      <td>24.0</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",
       "      <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>0.0</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",
       "      <td>34.7</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",
       "      <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>0.0</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",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX   \n",
       "0  0.00632  18.0   2.31   0.0  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  Price  \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  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</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",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</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",
       "      <td>25.000000</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",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM   \n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000  \\\n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  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       Price  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    float64\n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    float64\n",
      " 9   TAX      506 non-null    float64\n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  Price    506 non-null    float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = boston.data\n",
    "y = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain shape: (404, 13)\n",
      "xtest shape: (102, 13)\n",
      "ytrain shape: (404,)\n",
      "ytest shape: (102,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain, ytest = train_test_split(x,y, test_size=0.2, random_state= 42)\n",
    "print(\"xtrain shape:\", xtrain.shape)\n",
    "print(\"xtest shape:\", xtest.shape)\n",
    "print(\"ytrain shape:\", ytrain.shape)\n",
    "print(\"ytest shape:\", ytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "\n",
    "lr.fit(xtrain, ytrain)\n",
    "y_pred = lr.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ytest, y_pred, c = 'green')\n",
    "plt.xlabel(\"Price:in 1000's\")\n",
    "plt.ylabel(\"Predicted Value\")\n",
    "plt.title(\"True Values vs Predicted Values: Linear Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 24.291119474973673\n",
      "Mean Absolute Error: 3.189091965887854\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "mse = mean_squared_error(ytest, y_pred)\n",
    "mae = mean_absolute_error(ytest, y_pred)\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"Mean Absolute Error:\", mae)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### THANK YOU"
   ]
  }
 ],
 "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": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}