Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "capable-sherman",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # Data manipulation\n",
    "import numpy as np # Scientific Computation\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler # Data analysis\n",
    "from sklearn import linear_model # Data analysis\n",
    "from sklearn.linear_model import LinearRegression # Data analysis\n",
    "import sklearn.metrics as sm # Data analysis\n",
    "from sklearn.model_selection import train_test_split # Data analysis\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import pickle # rick / For serialization and deserialization of data from/to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "funny-sharp",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/student_scores.csv\", sep = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "constant-hypothesis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "competitive-divide",
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.8</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours  Scores\n",
       "0     2.5      21\n",
       "1     5.1      47\n",
       "2     3.2      27\n",
       "3     8.5      75\n",
       "4     3.5      30\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dirty-mechanics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "finnish-arthur",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vulnerable-robin",
   "metadata": {},
   "source": [
    "# Clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "coated-intervention",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to check null values in data\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "devoted-formation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD4CAYAAABPLjVeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAHBElEQVR4nO3bT8hldR3H8c/XJlHKimhjC4sgC0qxCQnBoCBbZFRgZSCBudAipFUUBS4qhHBTGgUFmWRBtFIoyj+DIBjpwn+VJZG1sSARCm0QJ38tnmONU1pNM5+nGV8veLi/59xzD78Dh/fzu+e5d9ZaAaDjhN2eAMDziegCFIkuQJHoAhSJLkDRnud68rwTPuCjDQD/pZuf+v4823NWugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFogtQJLoARaILUCS6AEWiC1AkugBFs9ba7Tk8b8zMpWutr+/2POBQrs0eK92uS3d7AvAsXJslogtQJLoARaLb5Z4Z/69cmyX+kQZQZKULUCS6AEWiexhm5rFDfr94Zr6yW/OBJJmZz87Mz2fmvpm5Z2besttz4p/t2e0J8A8zs2etdWC358GxZ2bOSfLuJHvXWk/MzCuSnPg/HM+1eJRY6R5hM/Pqmdm3rTZunZnTtu3fmpn3H7TfY9vj22bm9pm5MckvZuZFM/ODmbl3Zn42Mxfu0qlwbDk1ySNrrSeSZK31yFrr4Zk5e2bu2K6nO2fmlJk5aWaunZn7Z+bumXl78vd3bDfOzL4kt27X4je31909M+/d9nvDtu2e7Tp/7e6d9rHHSvfwnDwz9xz0+8uT3LiNr0ly3Vrrupm5JMnVSd73b463N8kb11oPzcwFSR5ea52fJDPz0iM6c45XNyW5YmYeTHJLku8l+cn2eOFa666ZeUmS/Uk+kWSttc6YmdcnuWlmTt+OszfJmWutR2fmyiT71lqXzMzLktw5M7ck+WiSL6+1vjMzJyZ5QfNEj3VWuodn/1rrrKd/klxx0HPnJPnuNv52knP/g+PdudZ6aBvfn+S8mfnizLx1rfWnIzZrjltrrceSvDk7X+f9Y3Zie1mS36+17tr2+fN2y+DcJNdv236Z5HdJno7uzWutR7fxO5N8eltg3JbkpCSnZSfmn5mZTyV51Vpr/1E/weOIlW7PgWx/5GbmhDzzftvjTw/WWg/OzN4k70ryhZm5da31uepMOSattf6anTjeNjP3J/n4YRzm8YPGk+SCtdavDtnngZn5aZLzk/xwZi5ba+07nDk/H1npHnl3JPnQNr4oye3b+LfZWYkkyXuSvPBfvXhmXpnkL2ut65NclZ23e/CcZuZ1h9xbPSvJA0lOnZmzt31OmZk92bkmL9q2nZ6d1euhYU2SHye5fGZm2/dN2+NrkvxmrXV1khuSnHlUTuo4ZaV75F2e5NqZ+WR23uZ9ZNv+jSQ3zMy9SX6UZ64oDnZGkqtm5qkkTyb52FGeL8eHFye5Zrv3eiDJr7Nzq+HabfvJ2bmf+44kX03ytW01fCDJxdsnHg495ueTfCnJfdu7s4ey8wmJDyb58Mw8meQPSa48uqd2fPE1YIAitxcAikQXoEh0AYpEF6BIdAGKRBegSHQBiv4GZIgMoLsi9MQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# same visualized\n",
    "# makes sence if you have null values\n",
    "sns.heatmap(df.isnull(), yticklabels= False, cbar=False, cmap= 'viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "delayed-township",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scores</th>\n",
       "      <td>0.976191</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours    Scores\n",
       "Hours   1.000000  0.976191\n",
       "Scores  0.976191  1.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "damaged-mainland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the matrix as a heat map\n",
    "plt.subplots(figsize = (10, 8))\n",
    "sns.heatmap(corr_matrix, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "common-composition",
   "metadata": {},
   "source": [
    "# Train Model\n",
    "\n",
    "#### Split data into independent X and Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "daily-browser",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['Hours'].values.reshape(-1, 1)\n",
    "y = df['Scores'].values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "latest-morris",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plot all\n",
    "df.plot(x='Hours', y='Scores', style='o')\n",
    "plt.title('Hours vs Percentage')\n",
    "plt.xlabel('Hours Studied')\n",
    "plt.ylabel('Percentage Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "precious-staff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 1)\n",
      "(20,)\n",
      "(5, 1)\n",
      "(5,)\n"
     ]
    }
   ],
   "source": [
    "X = df.iloc[:, :-1].values\n",
    "y = df.iloc[:, 1].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "# the shape of the subsets\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "governing-necklace",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating an instance of Linear Regression model\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "# get the calculated coefficients\n",
    "a = regressor.coef_\n",
    "b = regressor.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "hazardous-controversy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.91065648])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "sophisticated-translation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.018160041434662"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "spiritual-vinyl",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lJCrLLJKhajsxeuctiyHPipL+0Hf7QsSV9DOYEr9ImqvNXrDD+vbl6g6tio7v+fYH+j0xNG6xSWpS4hdJczWZGD1wl08gz+h7+BMA9H/mfog4V9zYMpGhSorQck6RNFc4ATpw4kAWrl5I2yZtGdR1ULkTo2t+2cRXD+7PhzfNiB7/thXz9lzGA2823Oy1krk0uSuSJY7v/BIvXX5C0XGPO1/j9Vk9QoxIEk2TuyJZasoHa9j4bN2ipD/5y4P57fhNSvpZTIlfJINd1fMu/vJDY+rmbALgqAc+45D/m0yDhvqvn830ty+SgW6+eiHkGXdFrgbgobcuhYjz1tTdQ45MUoEmd0UyzDMXncXNhzxbdPzo0qVcNqJFJWdItlGPXyQNxFKLZ7+dp0GecVaQ9C968lGIOBdfrqQvpanHL5LiCmvxFNarL6zFA9GlnD//uJFFT+zNtFs/B2DFL81YcdBCHnunQWgxS2pTj18kxVW2Sckp+7/ANm/VY/c20aR/5L/eovmFK+i4u5K+VEyJXyTFlVdzp/H8LszffgEv9D8VgAmzu7HhpALe/uzIZIcnaSihid/MmprZWDP70szmmtmBZratmU0ws2+CP7dJZAwi6a5szZ2Bs25ldff3i45PHP4F3e+YQP0tLMmRSbpKdI//AeBNd98V2BOYC1wLTHT3DsDE4Fgk49V0s5TCWjx7TrkZ7wC3nXIjAPe8fjlEnBcn7pbIsCUDJaxkg5k1AWYBf/ASNzGzr4Au7r7EzHYE3nf3XSq7lko2SLorO0EL0UJqsWw24gWO5+VQp07x/9X+37zBAzcdnbB4JTOEUbJhJ2AF8JSZfWJmT5hZI6CFuy8JXrMU0FozyXiVTdBW5pT9X8Ceq1OU9B+ZcDFEXElfaiWmxG9mO5vZFsHjLmb2dzNrWsVpdYF9gMfcfW/gV8oM6wS/CZT7K4eZ9TOz6WY2fcWKFbGEKZKyqrtZyrIl6yHPiiZvAb7rvJZLnnokIfFJdom1xz8O2GRm7YGhQBsgr4pzFgGL3H1qcDyW6BvBsmCIh+DP5eWd7O5D3b2zu3du3rx5jGGKpKbqbJZy+2nX0+K9LYuOzxnyJEScP/yxUcLik+wSa+IvcPeNwAnAQ+5+FbBjZSe4+1Lgf2ZWOH7fFfgCeBnoE7T1AcZXO2qRNBPLZimvv7gU8ozre91R1Lbp1AKe+uCcpMUp2SHWxJ9vZn8jmqhfDdrqxXDeZcAoM/sM2Au4HfgX0N3MvgG6BcciGa337r0ZetxQcpvkYhi5TXJLTex+cONh9Pi9uC916ctTIOLk1NUSTYm/mFb1mFlH4ELgQ3cfbWY7Aae6++BEBwha1SOZ66rzpnNX132Ljj/+tjP73vhxiBFJJqloVU/MyznNrAHQ1t2/indwVVHil0xTsMmpM6b0L9wv+v84sXfrkCKSTFSr5ZxmdhzRNflvBsd7mdnLcY1QJEuccfDIUkm/8INYSvqSLLFW57wZ2A94H8DdZ5nZHxIUk0hG+n7eb+w0rSEjLy5uW/SXdVwRUUE1Sa6YJ3fdfXWZtoJ4ByOSqe454wp2mla8queMR5+FiNO6nZK+JF+sPf45ZhYBcsysA/B3YEriwhLJDE8+8gPnbtOaK0rsa15wWgEjI1qtI+GJtcd/GfAnYD3RD26tBgYkKCaRjDDt1v04d5vicfu/PT0NIk6dHCV9CVeVid/McoDX3H2gu+8bfN3g7r8nIT6RhKtp1cyK9DpoCuQZ++0cXZb5wdxDIeKMfnvfKs4USY4qh3rcfZOZFZhZk3LG+UXSWlXbGlbHxnyn7gt1GH9Jcdtz6xdz+q2VfshdJOliHepZC8w2s+Fm9mDhVyIDE0mGmlbNLOvcLk9S94Xi/063j78OIs7p5yjpS+qJdXL3xeBLJKNUt2pmWXNn/8pus7fiyX7FbT8c8jvXR7aIR3giCRFTj9/dRwCjgRnBV17QJpLWqlM1s6xHz7mY3WZvVXR86oNjIOK0aqOkL6kt1k/udgG+AR4BHgW+NrNDExeWSHLEUjWzrIcGL4A84+LujwGQv7EufnoBz390aoXniKSSWMf47wGOdPfD3P1Q4CjgvsSFJZIcVVXNLOvzwZ24rE27ouPzxn5CvbPysTpaoinpI9bqnJ+5+x5VtSWKirRJ2LrtOYl3rjms6PjNT4/i6MFvhhiRSNUqKtIW6+TudDN7AhgZHPcGlIkl421YX0D9cTm8c01x2/ic5fQarF3hJH3FOtRzEdHds/4efH0RtIlkrIu6PUb9cTlFx/8cewtEnF6nKelLeot1qKcR8Lu7bwqOc4At3H1d5WfGh4Z6JJlmfbyGvb5pXKpt+RHr2X6H+iFFJFIztarHD0wESpYRbAC8E4/ARFLJk/3OLZX0e937EkRcSV8ySqyJf0t3X1t4EDxuWMnrRdLK4Bu/hTzj3C5PAbB6XWOIOOOn9wo5MpH4izXx/2pm+xQemFln4LfEhCSSXN/etzPX7Na+6Lj/W7Npcr7KUknmijXxDwBeMLPJZjYZeA64NGFRiSRBt07vQJ6xc4vvAPj3x8dDxHlgRKdwAxNJsEqXc5rZvsD/3P1jM9sVuAA4kejeu98nIT6RuFv36yYajq/LO9cXt01ovJIT7tsuvKBEkqiqHv8QYEPw+EDgeqJlG34GhiYwLpGE6H/0AzQcX9zfuWb0vyDidO+ppC/Zo6rEn+PuPwWPTwOGuvs4d78RaF/JeSIpZcoHqyDPeOCsAUVtq47OZ/Ar11R4TnnivWmLSBiqTPxmVtg96gq8W+K5WD/1KxKqUZf05i8/bFN03OPO1yDiNN22ev+ECzdtWbB6AY4Xbdqi5C/ppqrEPxr4wMzGE13FMxnAzNoT3XdXJGXd2P8ryDN6H5QHwNJVLSDivD6rRxVnli9em7aIhK3SLo+7DzKzicCOwNte/DHfOkQ3YBdJSYsfacmt+y8pOr5+ylxuf3jXWl2ztpu2iKSKKpdzuvtH7v5vd/+1RNvX7j4zsaGJVN8xe70BeUbLbaJJf/SU0yHitU76ULtNW0RSSazr+EVS2prVGyHPeOPq4mGcqW1+5m8Pj47bPWqyaYtIKlLil7R39XF3svVr9YqO//HsvRBx9j+kaVzvU91NW0RSVUzVOcOm6pxSnskTf+KQZaXX36/rtZGGjXIqOEMku9S2OqdIShk34KRSSf/owW9DxJX0RWKgxC9p5ab+cyDPOGm/FwH4fnk7iDhvfto93MBE0ogSv6SNVcOacsv+xQXU7pv3DTsNUMkokepS4peU16vzeMgzmjaKfmbwqQ/Ohojzj3+qaohITSjxS5XCqk/z08p8yDPGX358UdusDqs5Z8hTSbm/SKZS4pdKhVWf5sYTbmXbt4u3O7z4qUcg4uy1b+NKzhKRWGg5p1Sq3f3tWLB6wWbtuU1ymT9gftzvN+GVFXRfs32ptvUnbmKLLavXRxk1exQDJw5k4eqFtG3SlkFdB2m9vWQdLeeUGklmfZrXr+5RKukf//D7EPEaJX1V0RSpmBK/VCoZ9WmuPO9TyDN67PUGAHMWdYSI89KUw2p0PVXRFKlcwhO/meWY2Sdm9mpwvJOZTTWzeWY2xszqV3UNCU8i69N4gbPhmfrc3XWvorbHl3zPn66eU6vrqoqmSOWS0ePvD8wtcTwYuM/d2xPdwvG8JMQgNZSo+jQn7z8We64O9evmA/DYOxdCxLnwina1jllVNEUql9DJXTNrDYwABgGXA8cBK4Ad3H2jmR0I3OzuR1V2HU3uZo7Fi9bTctKWpdq+3GMtu3ZqFLd7FI7xlxzuaVivoQqqSdYJa3L3fuBqoCA43g5Y5e4bg+NFQKvyTjSzfmY23cymr1ixIsFhSjIMOnVgqaR/3tAnIOJxTfqgKpoiVUlYj9/MegI93P1iM+sCXAmcDXwUDPNgZm2AN9y9U0XXAfX4090Lzy7llJwdS7VtPKWAuvUspIhEskMYPf6DgL+a2XzgOeAI4AGgaYkN3FsDPyQwBgnZezccXirpH3v/fyDiSvoiIUpY4nf369y9tbu3A04H3nX33sB7wMnBy/oA4xMVg4TnzGOnQ55xeMf3AZj+3Z8h4rw27aBwAxORUNbxXwNcbmbziI75Dw8hBkmQgk0Oecazvfctahu6fCGdb9BQnUiqSErid/f33b1n8Pg7d9/P3du7+ynuvj4ZMUjiRQ7Ko86Y4n9S970xACJOvwFtwgtKRDZTt+qXiFRuwbe/kTu1IXmXFLfN+/M6/hFpEF5QIlIhlWyQWrm795XkTi3+ZO+Zjz0DEaf9Lkr6IqlKPX6pkRGP/UCfJq258tjitoLTCng2otU6IqlOPX6ptg9vOZA+TVoXHZ86fCpEnDo5lSf9sDZ0EZHSlPglZicc+hHkGQd2+AiAyV8eDBHn+Yn7VXmuSiWLpA5txCJV2rTRyXm+dB9h5LrFnHH+jhWcsblkb+giItqIRWro7MOeLpX0//XyNRDxaiV9UKlkkVSiyV0p1/x562g3rRFPX1DctvjQ37k2skWNrte2Sdtye/wqlSySfOrxy2Ye6nMZ7aYVV8w87aHnIOK0bF2zpA+J3dBFRKpHPX4p8tyTCzl9y1wuC3ZHKCgwLLKJMXFYollYElkboIuET5O7AsCnd+zJnrmfFR0PnDyDQY/tE2JEIlJbmtyVcl1wwiTIs6KkP2F2N4i4kr5IBtNQT5bK31BAvbE5DDmluG1ik2V0v2P78IISkaRQjz8LXdB1CPXG5hQd3zzuJog4XY9V0hfJBurxZ5Gv56zhj582Zsh5xW0/dl/PzZH64QUlIkmnHn8aqazWTVV1cIb17csfP21cdHzCfS9CxNmuee2SvurviKQfrepJE4W1btblrytqa1ivIUOPGwpQ4XPr3zuQc5vtXNS+9vdGNDpnLRaHIpqVxaRlmiLhq2hVjxJ/mqis1g1Q7nNfb92eDjvMKzoe9MlnDLxr96TEpPo7IuHTcs40V1mtm7LPHf7mCLwDRUl//Iy/QsTjmvSriklEUpcmd9NEVbVuFqxeQJ38+mzquAE69Cl6/j87rKTXPduFEpOIpCb1+NNEZbVuBnUdxGWf3hBN+oHrxtzOqN1HcvARiUn6VcUkIqlLPf40UVGtm8O26knrD5vS++Ti1+60sCO33dY24ROsqr8jkp40uZvGnr34TM48eGTR8SNzXuGSQT1rfd1Rs0cpmYtkgIomd9XjT0MfvPI1h63ZhTMPjh6v+KUZzS9cwSVxuHbZJZqFWyQCSv4iGUJj/Glm3r3tOWzNLkXHE+p/QfMLV8Tt+gMnDiy1Lh9gXf46Bk4cGLd7iEi4lPjTxJgRS/n3P06g/Q7fRo8/OhUiTveTd4vrfbREUyTzKfGnuPwNBfQ9YhhHbtiNHnu9zpCJ/Viw78+c9uCYhNyvoqWYWqIpkjmU+FPYjf2/YsqthzPs/H58umBPbpv5GRcMH0Juh6abvTZeNXO0RFMk82lyNwUtW7KBhy8ezA3H38a6DQ05f9gwHnvrXLrUL/99Op4TslqiKZL5tJwzzmq7FLLXQR8y6K996dRmTnQc/88PcFqfHSo9RzVzRKQ8Ws6ZBLXpeU//8BemDr2Of1/0GIt+ak3Pu1/h1ZmxrcnXhKyIVIfG+OOopkshj+/8EjvM6MhFXR/jobcv46cD58Sc9EETsiJSPUr8cVTdnvfIYYsZN+AkXrr8BH5auy2nDv+I/s88wF77bl2t+2pCVkSqQ4k/jmLteW9YX8CF3R7nONuNY/d6jevG3E7Lc2cw9r39anTf3rv3ZuhxQ8ltkoth5DbJ1WYoIlIhjfHH0aCug8rdkapkz/uaC+fSc/t+PH7uf5j4+RFM2fg4d4zvUOt79969txK9iMREiT+OKlsKuXjReob2v4Nbe93O2t+34uwhTzF8Yh+61o3DHogiItWg5ZxJcEzn/3DvaX3ZrdWX5E35G1v+5X5OjGxf7euoaqaIVIeWc4bgw0mr+HTEtbxx+RDmr8jlmDtf541Zx9ToWqqaKSLxosndBPAC56T9xpE7uyN9Dx/GPa9fzq+HfV7jpA+qmiki8ZOwxG9mbczsPTP7wszmmFn/oH1bM5tgZt8Ef26TqBhqqjZ1bz6ZsojxV57AuAEns2x1C84aOZUrRt7Dn/baqlYx6UNaIhIviezxbwSucPeOwAHAJWbWEbgWmOjuHYCJwXHKKBxSWbB6AY4XDalUlfw35hdwW59H2HluR47c/W2uyruTdhdMY9Sbmw2v1Yg+pCUi8ZKwxO/uS9x9ZvB4DTAXaAX0AkYELxsBHJ+oGGqiJkMqb475nGmDDuaGoy5l6rz9mbnjbO569Sq22a5e3OLSh7REJF6SMsZvZu2AvYGpQAt3XxI8tRRoUcE5/cxsuplNX7EifjtMVaU6Qyqrf/qdW0+5kSPW78Mfd/yawZOeodugtzn46J3jHpc+pCUi8ZLw5ZxmthXwATDI3V80s1Xu3rTE8z+7e6Xj/Mlczhlrpctn7v6A/enHLi2/5pnJZ3LARffwxz2aJyVGEZFYVLScM6E9fjOrB4wDRrn7i0HzMjPbMXh+R2B5ImOorqqGVBZ88zPD+vblrJZdqFc3n2Hz3uKsx55R0heRtJHIVT0GDAfmuvu9JZ56GegTPO4DjE9UDDVR0ZBK5E8R7hvwPFtM2I1zDn2KO1+9iq1Pn03ffx4ZdsgiItWSsKEeMzsYmAzMBgqC5uuJjvM/D7QFFgCnuvtPlV2rJkM98fyU6/RJC1ny8iUct8+rzPh+H/7X8gmOP2fvGl1LRCRZkv7JXXf/D1BRIZquibovxO9TrvkbNvGv8x5hQJeB7NaxgFteuYdrn/w7f26gDzyLSPrKyFo98diK8PW8z9huXl/2bz+NNz89iibdHuPA7jvFHIOISNhCmdwNS20+5frzz2AGzLqWnZp/z91TRnHUHW8o6YtIxsjIxF/TT7n+85+w7bbRx/2GD+WXw+Zy5cMRrI5KJ4tI5sjIxF/dT7nOmxft5d96a/T4uutg0Y+tad9xu0SHKiKSdBk5S1nZhiglucMJJ8D4EgtKf/yxuNcvIpKJMnJyNxaTJsFhhxUfjxgBZ50V11uIiIRKG7EENmyADh1gYTDP+4c/wNy5UL9+uHGJiCRLRo7xV+Tpp2GLLYqT/qRJ8O23Svoikl2yose/ciU0L1FK58QTYezYYNmmiEiWyfge/zXXlE768+bBuHFK+iKSvTI68V92Gdx5Z/TxzTdHV/HsHP9S+SIiaSWjh3p69IBZs+CVV6Bp07CjERFJDRmd+I85JvolIiLFMnqoR0RENqfELyKSZZT4RUSyjBK/iEiWUeIXEckySvwiIllGiV9EJMso8YuIZJm0qMdvZiuAzXdPL18zYGUCw6mpVIwrFWMCxVUdqRgTpGZcqRgTJDauXHdvXrYxLRJ/dZjZ9PI2HghbKsaVijGB4qqOVIwJUjOuVIwJwolLQz0iIllGiV9EJMtkYuIfGnYAFUjFuFIxJlBc1ZGKMUFqxpWKMUEIcWXcGL+IiFQuE3v8IiJSCSV+EZEskzGJ38yeNLPlZvZ52LEUMrM2ZvaemX1hZnPMrH/YMQGY2ZZmNs3MPg3iuiXsmAqZWY6ZfWJmr4YdSyEzm29ms81slplNDzueQmbW1MzGmtmXZjbXzA4MOZ5dgp9R4dcvZjYgzJgKmdk/gn/rn5vZaDPbMgVi6h/EMyfZP6eMGeM3s0OBtcAz7t4p7HgAzGxHYEd3n2lmWwMzgOPd/YuQ4zKgkbuvNbN6wH+A/u7+UZhxAZjZ5UBnoLG79ww7HogmfqCzu6fUh3/MbAQw2d2fMLP6QEN3XxVyWED0DRz4Adjf3WP98GWiYmlF9N94R3f/zcyeB15396dDjKkT8BywH7ABeBO40N3nJeP+GdPjd/dJwE9hx1GSuy9x95nB4zXAXKBVuFGBR60NDusFX6H3AMysNXAs8ETYsaQ6M2sCHAoMB3D3DamS9ANdgW/DTvol1AUamFldoCGwOOR4dgOmuvs6d98IfACcmKybZ0ziT3Vm1g7YG5gacihA0ZDKLGA5MMHdUyGu+4GrgYKQ4yjLgbfNbIaZ9Qs7mMBOwArgqWBo7AkzaxR2UCWcDowOOwgAd/8BuBtYCCwBVrv72+FGxefAIWa2nZk1BHoAbZJ1cyX+JDCzrYBxwAB3/yXseADcfZO77wW0BvYLfvUMjZn1BJa7+4ww46jAwe6+D3AMcEkwrBi2usA+wGPuvjfwK3BtuCFFBcNOfwVeCDsWADPbBuhF9M2yJdDIzM4IMyZ3nwsMBt4mOswzC9iUrPsr8SdYMIY+Dhjl7i+GHU9ZwfDAe8DRIYdyEPDXYDz9OeAIMxsZbkhRQY8Rd18O/JvouGzYFgGLSvymNpboG0EqOAaY6e7Lwg4k0A343t1XuHs+8CLwl5Bjwt2Hu/uf3f1Q4Gfg62TdW4k/gYJJ1OHAXHe/N+x4CplZczNrGjxuAHQHvgwzJne/zt1bu3s7osME77p7qL0yADNrFEzMEwylHEn01/RQuftS4H9mtkvQ1BUIddFACX8jRYZ5AguBA8ysYfB/sivR+bZQmdn2wZ9tiY7v5yXr3nWTdaNEM7PRQBegmZktAm5y9+HhRsVBwJnA7GA8HeB6d389vJAA2BEYEay8qAM87+4ps3wyxbQA/h3NF9QF8tz9zXBDKnIZMCoYWvkOOCfkeArfHLsDF4QdSyF3n2pmY4GZwEbgE1KjfMM4M9sOyAcuSebkfMYs5xQRkdhoqEdEJMso8YuIZBklfhGRLKPELyKSZZT4RUSyjBK/SMDM1pY5PtvMHg4rHpFEUeIXSbCgMJhIylDiF4mBmbUzs3fN7DMzmxh82hIze9rMTi7xurXBn13MbLKZvQx8EXwC+LVgD4TPzey0kL4Vkcz55K5IHDQo8QlrgG2Bl4PHDwEj3H2EmZ0LPAgcX8X19gE6ufv3ZnYSsNjdj4WissoioVCPX6TYb+6+V+EX8M8Szx1IcS2VZ4GDY7jeNHf/Png8G+huZoPN7BB3Xx23qEWqSYlfpHY2Evw/MrM6QP0Sz/1a+MDdvyb6G8Bs4DYzK/mmIpJUSvwisZlCtGooQG9gcvB4PvDn4PFfie5mthkzawmsc/eRwF2kTgllyUIa4xeJzWVEd7u6iujOV4WVMIcB483sU6Ibavxawfm7A3eZWQHRaowXJThekQqpOqeISJbRUI+ISJZR4hcRyTJK/CIiWUaJX0Qkyyjxi4hkGSV+EZEso8QvIpJl/h/dRzBEiTxyBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "# Visualise the Linear Regression \n",
    "plt.title('Linear Regression')\n",
    "plt.scatter(X, y, color='green')\n",
    "plt.plot(X_train, a*X_train + b, color='blue')\n",
    "plt.plot(X_test, y_pred, color='orange')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "infinite-connecticut",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.884145\n",
       "1      27  33.732261\n",
       "2      69  75.357018\n",
       "3      30  26.794801\n",
       "4      62  60.491033"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "orange-saturn",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9526947647057273"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2 = regressor.score(X, y)\n",
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dirty-nightmare",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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