Place_Data_Analysis.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e83611a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aabee1af",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>cgpa</th>\n",
       "      <th>package</th>\n",
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       "      <td>6.89</td>\n",
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       "      <th>3</th>\n",
       "      <td>7.42</td>\n",
       "      <td>3.67</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>5.89</td>\n",
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       "      <th>197</th>\n",
       "      <td>7.21</td>\n",
       "      <td>3.24</td>\n",
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       "      <td>7.63</td>\n",
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       "      <th>199</th>\n",
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       "      <td>2.33</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
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       "     cgpa  package\n",
       "0    6.89     3.26\n",
       "1    5.12     1.98\n",
       "2    7.82     3.25\n",
       "3    7.42     3.67\n",
       "4    6.94     3.57\n",
       "..    ...      ...\n",
       "195  6.93     2.46\n",
       "196  5.89     2.57\n",
       "197  7.21     3.24\n",
       "198  7.63     3.96\n",
       "199  6.22     2.33\n",
       "\n",
       "[200 rows x 2 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"dataset/place.txt\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1308421",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   cgpa     200 non-null    float64\n",
      " 1   package  200 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 3.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74deb9d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a918d319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cgpa       0\n",
       "package    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "75274e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder,OrdinalEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "096b5007",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(df.iloc[:,0])\n",
    "Y = pd.DataFrame(df.iloc[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "21247e61",
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   "outputs": [
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       "     cgpa\n",
       "0    6.89\n",
       "1    5.12\n",
       "2    7.82\n",
       "3    7.42\n",
       "4    6.94\n",
       "..    ...\n",
       "195  6.93\n",
       "196  5.89\n",
       "197  7.21\n",
       "198  7.63\n",
       "199  6.22\n",
       "\n",
       "[200 rows x 1 columns]"
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    "X"
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      "text/plain": [
       "     package\n",
       "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",
       "\n",
       "[200 rows x 1 columns]"
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     "execution_count": 9,
     "metadata": {},
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   "source": [
    "Y"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a48c5521",
   "metadata": {},
   "outputs": [],
   "source": [
    " X_train,X_test,Y_train,Y_test =train_test_split(X , Y , train_size=0.8, random_state=2)"
   ]
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   "cell_type": "code",
   "execution_count": 11,
   "id": "87b972bb",
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       "      <th>cgpa</th>\n",
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       "      <td>7.66</td>\n",
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       "<p>160 rows × 1 columns</p>\n",
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       "     cgpa\n",
       "137  7.14\n",
       "163  8.93\n",
       "111  5.42\n",
       "123  5.10\n",
       "109  7.77\n",
       "..    ...\n",
       "43   7.66\n",
       "22   6.14\n",
       "72   7.78\n",
       "15   7.25\n",
       "168  8.65\n",
       "\n",
       "[160 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "X_train"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ef9297d2",
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   "outputs": [
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       "      <td>7.15</td>\n",
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       "      <th>182</th>\n",
       "      <td>5.88</td>\n",
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       "      <th>85</th>\n",
       "      <td>4.79</td>\n",
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       "      <th>10</th>\n",
       "      <td>5.32</td>\n",
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       "      <th>54</th>\n",
       "      <td>6.86</td>\n",
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       "      <th>115</th>\n",
       "      <td>8.35</td>\n",
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       "      <th>35</th>\n",
       "      <td>6.87</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8.94</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>7.90</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.93</td>\n",
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       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>5.91</td>\n",
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       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>7.32</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.09</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.42</td>\n",
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       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>6.94</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.73</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.19</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>7.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>8.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>7.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>7.60</td>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>8.63</td>\n",
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       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>5.12</td>\n",
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       "      <th>178</th>\n",
       "      <td>8.15</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>7.36</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.31</td>\n",
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       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>6.60</td>\n",
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       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.59</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>7.47</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>7.93</td>\n",
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       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>6.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>6.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa\n",
       "112  8.58\n",
       "29   7.15\n",
       "182  5.88\n",
       "199  6.22\n",
       "193  4.57\n",
       "85   4.79\n",
       "10   5.32\n",
       "54   6.86\n",
       "115  8.35\n",
       "35   6.87\n",
       "12   8.94\n",
       "92   7.90\n",
       "13   6.93\n",
       "126  5.91\n",
       "174  7.32\n",
       "2    7.82\n",
       "44   5.09\n",
       "3    7.42\n",
       "113  6.94\n",
       "14   7.73\n",
       "23   6.19\n",
       "25   7.28\n",
       "6    6.73\n",
       "134  7.20\n",
       "165  8.21\n",
       "173  6.75\n",
       "45   7.87\n",
       "65   7.60\n",
       "48   8.63\n",
       "122  5.12\n",
       "178  8.15\n",
       "64   7.36\n",
       "9    8.31\n",
       "57   6.60\n",
       "78   6.59\n",
       "71   7.47\n",
       "128  7.93\n",
       "176  6.29\n",
       "131  6.37\n",
       "53   6.47"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa02cfd3",
   "metadata": {},
   "outputs": [
    {
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       "     package\n",
       "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",
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       "72      3.62\n",
       "15      3.23\n",
       "168     3.52\n",
       "\n",
       "[160 rows x 1 columns]"
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5ca8558a",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.65</td>\n",
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       "      <th>92</th>\n",
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       "      <th>44</th>\n",
       "      <td>1.86</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.67</td>\n",
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       "      <th>113</th>\n",
       "      <td>2.37</td>\n",
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       "      <td>2.56</td>\n",
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       "      <th>45</th>\n",
       "      <td>3.58</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>3.81</td>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4.09</td>\n",
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       "      <th>122</th>\n",
       "      <td>2.01</td>\n",
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       "      <th>178</th>\n",
       "      <td>3.63</td>\n",
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       "      <th>64</th>\n",
       "      <td>2.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.51</td>\n",
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       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1.94</td>\n",
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       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2.21</td>\n",
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       "      <th>71</th>\n",
       "      <td>3.34</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>3.34</td>\n",
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       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3.23</td>\n",
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       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     package\n",
       "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"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dde0397d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "38483fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f69feedb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a9a89816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.89111601],\n",
       "       [3.09324469],\n",
       "       [2.38464568],\n",
       "       [2.57434935],\n",
       "       [1.6537286 ],\n",
       "       [1.77647803],\n",
       "       [2.07219258],\n",
       "       [2.93143862],\n",
       "       [3.76278706],\n",
       "       [2.93701814],\n",
       "       [4.09197872],\n",
       "       [3.51170867],\n",
       "       [2.97049525],\n",
       "       [2.40138424],\n",
       "       [3.18809652],\n",
       "       [3.46707251],\n",
       "       [1.94386362],\n",
       "       [3.24389172],\n",
       "       [2.97607477],\n",
       "       [3.41685683],\n",
       "       [2.55761079],\n",
       "       [3.16577844],\n",
       "       [2.85890486],\n",
       "       [3.12114229],\n",
       "       [3.68467378],\n",
       "       [2.8700639 ],\n",
       "       [3.49497011],\n",
       "       [3.34432308],\n",
       "       [3.91901361],\n",
       "       [1.96060218],\n",
       "       [3.65119666],\n",
       "       [3.2104146 ],\n",
       "       [3.74046898],\n",
       "       [2.7863711 ],\n",
       "       [2.78079158],\n",
       "       [3.27178932],\n",
       "       [3.52844723],\n",
       "       [2.61340599],\n",
       "       [2.65804215],\n",
       "       [2.71383735]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3a5db3a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21bdea09af0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['cgpa'],df['package'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bea8a544",
   "metadata": {},
   "source": [
    "#  Model Accuracy Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8c81ebf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7733110961245032"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7e84d019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.780730147510384"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d32c3b",
   "metadata": {},
   "source": [
    "### MEAN ABSOLUTE ERROR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0ed5f95c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "44316ca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2884710931878175"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9081498",
   "metadata": {},
   "source": [
    "### MEAN SQUARED ERROR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b4dd04a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ba68f545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12129235313495527"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1394db0c",
   "metadata": {},
   "source": [
    "### ROOT MEAN SQUARED ERROR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "aec18cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34827051717731616"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "import sklearn.metrics\n",
    "mse = sklearn.metrics.mean_squared_error(Y_test, y_pred)\n",
    "rmse = math.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e594804e",
   "metadata": {},
   "source": [
    "### R-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0a9697bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bf0b0db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.780730147510384"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3726326",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}