OneHotEncoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "##### At the same level as this notebook on github, there is an Exercise folder that contains carprices.csv. This file has car sell prices for 3 different models. First plot data points on a scatter plot chart to see if linear regression model can be applied. If yes, then build a model that can answer following questions,\n",
    "\n",
    "1) Predict price of a mercedez benz that is 4 yr old with mileage 45000\n",
    "\n",
    "2) Predict price of a BMW X5 that is 7 yr old with mileage 86000\n",
    "\n",
    "3) Tell me the score (accuracy) of your model. (Hint: use LinearRegression().score())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.compose import ColumnTransformer\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>Car Model</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Sell Price($)</th>\n",
       "      <th>Age(yrs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW X5</td>\n",
       "      <td>69000</td>\n",
       "      <td>18000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW X5</td>\n",
       "      <td>35000</td>\n",
       "      <td>34000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW X5</td>\n",
       "      <td>57000</td>\n",
       "      <td>26100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW X5</td>\n",
       "      <td>22500</td>\n",
       "      <td>40000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BMW X5</td>\n",
       "      <td>46000</td>\n",
       "      <td>31500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Audi A5</td>\n",
       "      <td>59000</td>\n",
       "      <td>29400</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Audi A5</td>\n",
       "      <td>52000</td>\n",
       "      <td>32000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Audi A5</td>\n",
       "      <td>72000</td>\n",
       "      <td>19300</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Audi A5</td>\n",
       "      <td>91000</td>\n",
       "      <td>12000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mercedez Benz C class</td>\n",
       "      <td>67000</td>\n",
       "      <td>22000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mercedez Benz C class</td>\n",
       "      <td>83000</td>\n",
       "      <td>20000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mercedez Benz C class</td>\n",
       "      <td>79000</td>\n",
       "      <td>21000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mercedez Benz C class</td>\n",
       "      <td>59000</td>\n",
       "      <td>33000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Car Model  Mileage  Sell Price($)  Age(yrs)\n",
       "0                  BMW X5    69000          18000         6\n",
       "1                  BMW X5    35000          34000         3\n",
       "2                  BMW X5    57000          26100         5\n",
       "3                  BMW X5    22500          40000         2\n",
       "4                  BMW X5    46000          31500         4\n",
       "5                 Audi A5    59000          29400         5\n",
       "6                 Audi A5    52000          32000         5\n",
       "7                 Audi A5    72000          19300         6\n",
       "8                 Audi A5    91000          12000         8\n",
       "9   Mercedez Benz C class    67000          22000         6\n",
       "10  Mercedez Benz C class    83000          20000         7\n",
       "11  Mercedez Benz C class    79000          21000         7\n",
       "12  Mercedez Benz C class    59000          33000         5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('./Datasets/carprices.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "price=df[['Sell Price($)']].values\n",
    "age=df[['Age(yrs)']].values\n",
    "mileage=df[['Mileage']].values\n",
    "\n",
    "# plt.scatter(age,price,color='blue')\n",
    "\n",
    "plt.scatter(age,mileage,color='red')\n",
    "# plt.legend(['Price','Mileage','Age'])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mileage')\n",
    "plt.title('Car Mileage Prediction')\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(age,price,color='blue')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Car Price Prediction')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# mileage\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car Model</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Sell Price($)</th>\n",
       "      <th>Age(yrs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>69000</td>\n",
       "      <td>18000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>35000</td>\n",
       "      <td>34000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>57000</td>\n",
       "      <td>26100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>22500</td>\n",
       "      <td>40000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>46000</td>\n",
       "      <td>31500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>59000</td>\n",
       "      <td>29400</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>52000</td>\n",
       "      <td>32000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>72000</td>\n",
       "      <td>19300</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>91000</td>\n",
       "      <td>12000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>67000</td>\n",
       "      <td>22000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>83000</td>\n",
       "      <td>20000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>79000</td>\n",
       "      <td>21000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>59000</td>\n",
       "      <td>33000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Car Model  Mileage  Sell Price($)  Age(yrs)\n",
       "0           1    69000          18000         6\n",
       "1           1    35000          34000         3\n",
       "2           1    57000          26100         5\n",
       "3           1    22500          40000         2\n",
       "4           1    46000          31500         4\n",
       "5           0    59000          29400         5\n",
       "6           0    52000          32000         5\n",
       "7           0    72000          19300         6\n",
       "8           0    91000          12000         8\n",
       "9           2    67000          22000         6\n",
       "10          2    83000          20000         7\n",
       "11          2    79000          21000         7\n",
       "12          2    59000          33000         5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le=LabelEncoder()\n",
    "dfle=df\n",
    "dfle['Car Model']=le.fit_transform(dfle['Car Model'])\n",
    "dfle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    1,     6, 69000],\n",
       "       [    1,     3, 35000],\n",
       "       [    1,     5, 57000],\n",
       "       [    1,     2, 22500],\n",
       "       [    1,     4, 46000],\n",
       "       [    0,     5, 59000],\n",
       "       [    0,     5, 52000],\n",
       "       [    0,     6, 72000],\n",
       "       [    0,     8, 91000],\n",
       "       [    2,     6, 67000],\n",
       "       [    2,     7, 83000],\n",
       "       [    2,     7, 79000],\n",
       "       [    2,     5, 59000]], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=dfle[['Car Model','Age(yrs)','Mileage']].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18000],\n",
       "       [34000],\n",
       "       [26100],\n",
       "       [40000],\n",
       "       [31500],\n",
       "       [29400],\n",
       "       [32000],\n",
       "       [19300],\n",
       "       [12000],\n",
       "       [22000],\n",
       "       [20000],\n",
       "       [21000],\n",
       "       [33000]], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=dfle[['Sell Price($)']].values\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\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-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 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-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;Car Model&#x27;, OneHotEncoder(), [0])])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;Car Model&#x27;, OneHotEncoder(), [0])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">Car Model</label><div class=\"sk-toggleable__content \"><pre>[0]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">remainder</label><div class=\"sk-toggleable__content \"><pre></pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">passthrough</label><div class=\"sk-toggleable__content \"><pre>passthrough</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(remainder='passthrough',\n",
       "                  transformers=[('Car Model', OneHotEncoder(), [0])])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct=ColumnTransformer([('Car Model',OneHotEncoder(),[0])],remainder='passthrough')\n",
    "ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00e+00, 1.00e+00, 0.00e+00, 6.00e+00, 6.90e+04],\n",
       "       [0.00e+00, 1.00e+00, 0.00e+00, 3.00e+00, 3.50e+04],\n",
       "       [0.00e+00, 1.00e+00, 0.00e+00, 5.00e+00, 5.70e+04],\n",
       "       [0.00e+00, 1.00e+00, 0.00e+00, 2.00e+00, 2.25e+04],\n",
       "       [0.00e+00, 1.00e+00, 0.00e+00, 4.00e+00, 4.60e+04],\n",
       "       [1.00e+00, 0.00e+00, 0.00e+00, 5.00e+00, 5.90e+04],\n",
       "       [1.00e+00, 0.00e+00, 0.00e+00, 5.00e+00, 5.20e+04],\n",
       "       [1.00e+00, 0.00e+00, 0.00e+00, 6.00e+00, 7.20e+04],\n",
       "       [1.00e+00, 0.00e+00, 0.00e+00, 8.00e+00, 9.10e+04],\n",
       "       [0.00e+00, 0.00e+00, 1.00e+00, 6.00e+00, 6.70e+04],\n",
       "       [0.00e+00, 0.00e+00, 1.00e+00, 7.00e+00, 8.30e+04],\n",
       "       [0.00e+00, 0.00e+00, 1.00e+00, 7.00e+00, 7.90e+04],\n",
       "       [0.00e+00, 0.00e+00, 1.00e+00, 5.00e+00, 5.90e+04]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=ct.fit_transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00e+00, 0.00e+00, 6.00e+00, 6.90e+04],\n",
       "       [1.00e+00, 0.00e+00, 3.00e+00, 3.50e+04],\n",
       "       [1.00e+00, 0.00e+00, 5.00e+00, 5.70e+04],\n",
       "       [1.00e+00, 0.00e+00, 2.00e+00, 2.25e+04],\n",
       "       [1.00e+00, 0.00e+00, 4.00e+00, 4.60e+04],\n",
       "       [0.00e+00, 0.00e+00, 5.00e+00, 5.90e+04],\n",
       "       [0.00e+00, 0.00e+00, 5.00e+00, 5.20e+04],\n",
       "       [0.00e+00, 0.00e+00, 6.00e+00, 7.20e+04],\n",
       "       [0.00e+00, 0.00e+00, 8.00e+00, 9.10e+04],\n",
       "       [0.00e+00, 1.00e+00, 6.00e+00, 6.70e+04],\n",
       "       [0.00e+00, 1.00e+00, 7.00e+00, 8.30e+04],\n",
       "       [0.00e+00, 1.00e+00, 7.00e+00, 7.90e+04],\n",
       "       [0.00e+00, 1.00e+00, 5.00e+00, 5.90e+04]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xe=X[:,1:]\n",
    "Xe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" 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/1.5/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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression()\n",
    "model.fit(Xe,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BMW X5 -> 1,0\n",
    "Audi A5 -> 0,0\n",
    "Mercedez Benz C class -> 0,1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Predict price of a mercedez benz that is 4 yr old with mileage 45000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[36991.31721061]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[0,1,4,45000]])\n",
    "# 0 1 means Mercedes Benz C class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[28791.]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[0,0,5]])\n",
    "# 0 0 means Audi A5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) Predict price of a BMW X5 that is 7 yr old with mileage 86000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11080.74313219]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[1,0,7,86000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9417050937281082"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(Xe,Y)"
   ]
  }
 ],
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