linear-regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.3</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.6</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6.1</td>\n",
       "      <td>63</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\n",
       "25    1.3      19\n",
       "26    2.6      30\n",
       "27    6.1      63"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('studyhour.csv')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28 entries, 0 to 27\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   28 non-null     float64\n",
      " 1   Scores  28 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 576.0 bytes\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(kind='scatter', x='Hours', y='Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# membuat train dan test split\n",
    "x_train, x_test, y_train, y_test = train_test_split(dataset['Hours'], dataset['Scores'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18    6.1\n",
       "16    2.5\n",
       "14    1.1\n",
       "21    4.8\n",
       "23    6.9\n",
       "11    5.9\n",
       "6     9.2\n",
       "12    4.5\n",
       "5     1.5\n",
       "17    1.9\n",
       "19    7.4\n",
       "20    2.7\n",
       "9     2.7\n",
       "26    2.6\n",
       "7     5.5\n",
       "8     8.3\n",
       "0     2.5\n",
       "4     3.5\n",
       "10    7.7\n",
       "13    3.3\n",
       "3     8.5\n",
       "Name: Hours, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18    67\n",
       "16    30\n",
       "14    17\n",
       "21    54\n",
       "23    76\n",
       "11    62\n",
       "6     88\n",
       "12    41\n",
       "5     20\n",
       "17    24\n",
       "19    69\n",
       "20    30\n",
       "9     25\n",
       "26    30\n",
       "7     60\n",
       "8     81\n",
       "0     21\n",
       "4     30\n",
       "10    85\n",
       "13    42\n",
       "3     75\n",
       "Name: Scores, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     3.2\n",
       "27    6.1\n",
       "25    1.3\n",
       "22    3.8\n",
       "15    8.9\n",
       "1     5.1\n",
       "24    7.8\n",
       "Name: Hours, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     27\n",
       "27    63\n",
       "25    19\n",
       "22    35\n",
       "15    95\n",
       "1     47\n",
       "24    86\n",
       "Name: Scores, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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(x_train, y_train, label='Training Dataset', color='b')\n",
    "plt.scatter(x_test, y_test, label='Test Dataset', color='g', alpha=0.5)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18    6.1\n",
      "16    2.5\n",
      "14    1.1\n",
      "21    4.8\n",
      "23    6.9\n",
      "11    5.9\n",
      "6     9.2\n",
      "12    4.5\n",
      "5     1.5\n",
      "17    1.9\n",
      "19    7.4\n",
      "20    2.7\n",
      "9     2.7\n",
      "26    2.6\n",
      "7     5.5\n",
      "8     8.3\n",
      "0     2.5\n",
      "4     3.5\n",
      "10    7.7\n",
      "13    3.3\n",
      "3     8.5\n",
      "Name: Hours, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.1 2.5 1.1 4.8 6.9 5.9 9.2 4.5 1.5 1.9 7.4 2.7 2.7 2.6 5.5 8.3 2.5 3.5\n",
      " 7.7 3.3 8.5]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "b = x_train.values\n",
    "print(b)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.1]\n",
      " [2.5]\n",
      " [1.1]\n",
      " [4.8]\n",
      " [6.9]\n",
      " [5.9]\n",
      " [9.2]\n",
      " [4.5]\n",
      " [1.5]\n",
      " [1.9]\n",
      " [7.4]\n",
      " [2.7]\n",
      " [2.7]\n",
      " [2.6]\n",
      " [5.5]\n",
      " [8.3]\n",
      " [2.5]\n",
      " [3.5]\n",
      " [7.7]\n",
      " [3.3]\n",
      " [8.5]]\n"
     ]
    }
   ],
   "source": [
    "b = b.reshape(-1,1)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18    67\n",
      "16    30\n",
      "14    17\n",
      "21    54\n",
      "23    76\n",
      "11    62\n",
      "6     88\n",
      "12    41\n",
      "5     20\n",
      "17    24\n",
      "19    69\n",
      "20    30\n",
      "9     25\n",
      "26    30\n",
      "7     60\n",
      "8     81\n",
      "0     21\n",
      "4     30\n",
      "10    85\n",
      "13    42\n",
      "3     75\n",
      "Name: Scores, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[67 30 17 54 76 62 88 41 20 24 69 30 25 30 60 81 21 30 85 42 75]\n"
     ]
    }
   ],
   "source": [
    "print(y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Membuat model\n",
    "LR = LinearRegression()\n",
    "LR.fit(x_train.values.reshape(-1,1), y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([34.78714063, 61.73896307, 17.12905007, 40.36337975, 87.76141232,\n",
       "       52.44523119, 77.53830725])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# membuat prediksi\n",
    "prediction = LR.predict(x_test.values.reshape(-1,1))\n",
    "prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_test, prediction, label='garis Linear Regression', color='r')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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(x_test, y_test, label='Hasil data test', color='g')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_test, prediction, label='garis Linear Regression', color='r')\n",
    "plt.scatter(x_test, y_test, label='Hasil data test', color='g')\n",
    "plt.xlabel('Jam belajar')\n",
    "plt.ylabel('Score murid')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modellr2 = LinearRegression()\n",
    "modellr2.fit(dataset['Hours'].values.reshape(-1,1), dataset['Scores'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27.39521568, 52.55644537, 34.1693929 , 85.45959188, 37.07261171,\n",
       "       17.71781965, 92.2337691 , 56.42740378, 83.52411267, 29.33069489,\n",
       "       77.71767505, 60.29836219, 46.75000775, 35.13713251, 13.84686123,\n",
       "       89.33055029, 27.39521568, 21.58877806, 62.2338414 , 74.81445624,\n",
       "       29.33069489, 49.65322656, 39.97583052, 69.97575823, 78.68541466,\n",
       "       15.78234044, 28.36295528, 62.2338414 ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediksi = modellr2.predict(dataset['Hours'].values.reshape(-1,1))\n",
    "prediksi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dataset['Hours'], prediksi, label='garis Linear Regression', color='r')\n",
    "plt.scatter(dataset['Hours'], dataset['Scores'], label='data aktual', color='g')\n",
    "plt.xlabel('Jam belajar')\n",
    "plt.ylabel('Score murid')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}