Multiple_Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ddca0b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "848ea435",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cgpa</th>\n",
       "      <th>Interview_marks</th>\n",
       "      <th>package</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.89</td>\n",
       "      <td>26</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.12</td>\n",
       "      <td>38</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.82</td>\n",
       "      <td>40</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.42</td>\n",
       "      <td>8</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.94</td>\n",
       "      <td>17</td>\n",
       "      <td>3.57</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>6.93</td>\n",
       "      <td>44</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>5.89</td>\n",
       "      <td>34</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>7.21</td>\n",
       "      <td>27</td>\n",
       "      <td>3.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>7.63</td>\n",
       "      <td>44</td>\n",
       "      <td>3.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>6.22</td>\n",
       "      <td>25</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  Interview_marks  package\n",
       "0    6.89               26     3.26\n",
       "1    5.12               38     1.98\n",
       "2    7.82               40     3.25\n",
       "3    7.42                8     3.67\n",
       "4    6.94               17     3.57\n",
       "..    ...              ...      ...\n",
       "195  6.93               44     2.46\n",
       "196  5.89               34     2.57\n",
       "197  7.21               27     3.24\n",
       "198  7.63               44     3.96\n",
       "199  6.22               25     2.33\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"dataset/cgpa_marks_package.csv\")\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "24490ca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f487a8a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 3 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   cgpa             200 non-null    float64\n",
      " 1   Interview_marks  200 non-null    int64  \n",
      " 2   package          200 non-null    float64\n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 4.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a82e776f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cgpa</th>\n",
       "      <th>Interview_marks</th>\n",
       "      <th>package</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.990500</td>\n",
       "      <td>31.745000</td>\n",
       "      <td>2.996050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.069409</td>\n",
       "      <td>18.972606</td>\n",
       "      <td>0.691644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.260000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.190000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.487500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.965000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.995000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.737500</td>\n",
       "      <td>43.250000</td>\n",
       "      <td>3.492500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.580000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>4.620000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cgpa  Interview_marks     package\n",
       "count  200.000000       200.000000  200.000000\n",
       "mean     6.990500        31.745000    2.996050\n",
       "std      1.069409        18.972606    0.691644\n",
       "min      4.260000         0.000000    1.370000\n",
       "25%      6.190000        17.000000    2.487500\n",
       "50%      6.965000        28.000000    2.995000\n",
       "75%      7.737500        43.250000    3.492500\n",
       "max      9.580000        94.000000    4.620000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "01e3cee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cgpa               0\n",
       "Interview_marks    0\n",
       "package            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b532329d",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>cgpa</th>\n",
       "      <th>Interview_marks</th>\n",
       "      <th>package</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.89</td>\n",
       "      <td>26</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.12</td>\n",
       "      <td>38</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.82</td>\n",
       "      <td>40</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.42</td>\n",
       "      <td>8</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.94</td>\n",
       "      <td>17</td>\n",
       "      <td>3.57</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   cgpa  Interview_marks  package\n",
       "0  6.89               26     3.26\n",
       "1  5.12               38     1.98\n",
       "2  7.82               40     3.25\n",
       "3  7.42                8     3.67\n",
       "4  6.94               17     3.57"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9eab8b4",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cgpa</th>\n",
       "      <th>Interview_marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.89</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.12</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.82</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.42</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.94</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>6.93</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>5.89</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>7.21</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>7.63</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>6.22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  Interview_marks\n",
       "0    6.89               26\n",
       "1    5.12               38\n",
       "2    7.82               40\n",
       "3    7.42                8\n",
       "4    6.94               17\n",
       "..    ...              ...\n",
       "195  6.93               44\n",
       "196  5.89               34\n",
       "197  7.21               27\n",
       "198  7.63               44\n",
       "199  6.22               25\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.DataFrame(df.iloc[:,0:2])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef7bdc66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      3.26\n",
       "1      1.98\n",
       "2      3.25\n",
       "3      3.67\n",
       "4      3.57\n",
       "       ... \n",
       "195    2.46\n",
       "196    2.57\n",
       "197    3.24\n",
       "198    3.96\n",
       "199    2.33\n",
       "Name: package, Length: 200, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = pd.DataFrame(df.iloc[:,-1])\n",
    "y = df.iloc[:,-1]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc45ea41",
   "metadata": {},
   "outputs": [],
   "source": [
    " x_train , x_test , y_train , y_test = train_test_split(x , y , test_size=0.2 , random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a253baa9",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>cgpa</th>\n",
       "      <th>Interview_marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>7.14</td>\n",
       "      <td>53</td>\n",
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       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>8.93</td>\n",
       "      <td>42</td>\n",
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       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>5.42</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>5.10</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.77</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7.66</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6.14</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>7.78</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.25</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>8.65</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  Interview_marks\n",
       "137  7.14               53\n",
       "163  8.93               42\n",
       "111  5.42               33\n",
       "123  5.10               52\n",
       "109  7.77               21\n",
       "..    ...              ...\n",
       "43   7.66               63\n",
       "22   6.14               54\n",
       "72   7.78               19\n",
       "15   7.25               21\n",
       "168  8.65               37\n",
       "\n",
       "[160 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d4a0beeb",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cgpa</th>\n",
       "      <th>Interview_marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>8.58</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>5.88</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>6.22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>4.57</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>4.79</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.32</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.86</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>8.35</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>6.87</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8.94</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>7.90</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.93</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>5.91</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>7.32</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.82</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.09</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.42</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>6.94</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.73</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.19</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.28</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.73</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>7.20</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>8.21</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>6.75</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>7.87</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>7.60</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>8.63</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>5.12</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>8.15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>7.36</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.31</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>6.60</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.59</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>7.47</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>7.93</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6.29</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>6.37</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>6.47</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  Interview_marks\n",
       "112  8.58               15\n",
       "29   7.15               15\n",
       "182  5.88               76\n",
       "199  6.22               25\n",
       "193  4.57               14\n",
       "85   4.79               28\n",
       "10   5.32               16\n",
       "54   6.86               36\n",
       "115  8.35               37\n",
       "35   6.87               12\n",
       "12   8.94               16\n",
       "92   7.90               63\n",
       "13   6.93               26\n",
       "126  5.91               37\n",
       "174  7.32               21\n",
       "2    7.82               40\n",
       "44   5.09               55\n",
       "3    7.42                8\n",
       "113  6.94               10\n",
       "14   7.73               43\n",
       "23   6.19               11\n",
       "25   7.28               58\n",
       "6    6.73               11\n",
       "134  7.20               93\n",
       "165  8.21                7\n",
       "173  6.75               48\n",
       "45   7.87               10\n",
       "65   7.60               28\n",
       "48   8.63               42\n",
       "122  5.12               51\n",
       "178  8.15               39\n",
       "64   7.36               52\n",
       "9    8.31               94\n",
       "57   6.60               24\n",
       "78   6.59               12\n",
       "71   7.47               20\n",
       "128  7.93               25\n",
       "176  6.29               30\n",
       "131  6.37               19\n",
       "53   6.47               33"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2012738f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137    3.13\n",
       "163    3.91\n",
       "111    2.25\n",
       "123    2.30\n",
       "109    4.06\n",
       "       ... \n",
       "43     2.76\n",
       "22     2.30\n",
       "72     3.62\n",
       "15     3.23\n",
       "168    3.52\n",
       "Name: package, Length: 160, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5f797704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112    4.10\n",
       "29     3.49\n",
       "182    2.08\n",
       "199    2.33\n",
       "193    1.94\n",
       "85     1.48\n",
       "10     1.86\n",
       "54     3.09\n",
       "115    4.21\n",
       "35     2.87\n",
       "12     3.65\n",
       "92     4.00\n",
       "13     2.89\n",
       "126    2.60\n",
       "174    2.99\n",
       "2      3.25\n",
       "44     1.86\n",
       "3      3.67\n",
       "113    2.37\n",
       "14     3.42\n",
       "23     2.48\n",
       "25     3.65\n",
       "6      2.60\n",
       "134    2.83\n",
       "165    4.08\n",
       "173    2.56\n",
       "45     3.58\n",
       "65     3.81\n",
       "48     4.09\n",
       "122    2.01\n",
       "178    3.63\n",
       "64     2.92\n",
       "9      3.51\n",
       "57     1.94\n",
       "78     2.21\n",
       "71     3.34\n",
       "128    3.34\n",
       "176    3.23\n",
       "131    2.01\n",
       "53     2.61\n",
       "Name: package, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "333d17a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "80ce9230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fbe5afab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.89979454, 3.10237603, 2.36034882, 2.57822923, 1.66423151,\n",
       "       1.77914693, 2.08134881, 2.92901527, 3.7593373 , 2.94790193,\n",
       "       4.09998866, 3.49398191, 2.97359571, 2.39870713, 3.19384646,\n",
       "       3.4621268 , 1.93146344, 3.25681974, 2.9880456 , 3.41027583,\n",
       "       2.56926451, 3.151021  , 2.87038758, 3.08699931, 3.69790624,\n",
       "       2.86102024, 3.50664644, 3.34610218, 3.91270221, 1.95041088,\n",
       "       3.64670105, 3.1989594 , 3.70541992, 2.79068524, 2.79176404,\n",
       "       3.27804635, 3.53178562, 2.61449072, 2.6652021 , 2.71320129])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "40765f37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7805012233012033"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f0316a66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE : 0.2873891848877421\n"
     ]
    }
   ],
   "source": [
    "print(\"MAE :\",mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dc4daefe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE : 0.12141898593789571\n"
     ]
    }
   ],
   "source": [
    "print(\"MSE :\",mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fb728da3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.536086919153734\n"
     ]
    }
   ],
   "source": [
    "print(\"RMSE :\",math.sqrt(mean_absolute_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7866d5ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_Score : 0.7805012233012033\n"
     ]
    }
   ],
   "source": [
    "print(\"R2_Score :\",r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77396847",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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