main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "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>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>...</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
       "0       1          3        alfa-romero giulia      gas        std        two   \n",
       "1       2          3       alfa-romero stelvio      gas        std        two   \n",
       "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
       "3       4          2               audi 100 ls      gas        std       four   \n",
       "4       5          2                audi 100ls      gas        std       four   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
       "0  convertible        rwd          front       88.6  ...         130   \n",
       "1  convertible        rwd          front       88.6  ...         130   \n",
       "2    hatchback        rwd          front       94.5  ...         152   \n",
       "3        sedan        fwd          front       99.8  ...         109   \n",
       "4        sedan        4wd          front       99.4  ...         136   \n",
       "\n",
       "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
       "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
       "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
       "4        mpfi       3.19    3.40              8.0        115     5500      18   \n",
       "\n",
       "   highwaympg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "4          22  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/CarPrice_Assignment.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['car_ID', 'symboling', 'CarName', 'fueltype', 'aspiration',\n",
       "       'doornumber', 'carbody', 'drivewheel', 'enginelocation', 'wheelbase',\n",
       "       'carlength', 'carwidth', 'carheight', 'curbweight', 'enginetype',\n",
       "       'cylindernumber', 'enginesize', 'fuelsystem', 'boreratio', 'stroke',\n",
       "       'compressionratio', 'horsepower', 'peakrpm', 'citympg', 'highwaympg',\n",
       "       'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df.loc[::, 'horsepower'].values\n",
    "y = df.loc[::, 'price'].values\n",
    "\n",
    "# Plot... I guess\n",
    "plt.scatter(X, y, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = X.reshape(-1,1)\n",
    "y = y.reshape(-1,1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y , test_size=.2)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "plt.scatter(X_test, y_test, color='b')\n",
    "plt.plot(X_test, y_pred, color='m')\n",
    "\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Horsepower')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The horsepower a vehicle should have if the price is 50000 : [[8499105.40282572]]\n"
     ]
    }
   ],
   "source": [
    "print(f\"The horsepower a vehicle should have if the price is 50000 : {model.predict([[50000]])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4396439287418876"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = model.score(X_test, y_test)\n",
    "score"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hehe",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}