co2_emissions_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0092cce7-ac8a-4236-b35b-0ee06c6ceb52",
   "metadata": {},
   "source": [
    "## CO2 Emissions \n",
    "### Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b1cb4fc0-acf0-4201-a2ef-c6ce5688ae31",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "7ea5575e-fc13-4939-bc09-ef6af45bb054",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"FuelConsumption.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "65020f4d-e082-443a-862f-6304456507ff",
   "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>MODELYEAR</th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1067.0</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.346298</td>\n",
       "      <td>5.794752</td>\n",
       "      <td>13.296532</td>\n",
       "      <td>9.474602</td>\n",
       "      <td>11.580881</td>\n",
       "      <td>26.441425</td>\n",
       "      <td>256.228679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.415895</td>\n",
       "      <td>1.797447</td>\n",
       "      <td>4.101253</td>\n",
       "      <td>2.794510</td>\n",
       "      <td>3.485595</td>\n",
       "      <td>7.468702</td>\n",
       "      <td>63.372304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>10.850000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>294.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>488.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
       "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
       "mean      2014.0     3.346298     5.794752             13.296532   \n",
       "std          0.0     1.415895     1.797447              4.101253   \n",
       "min       2014.0     1.000000     3.000000              4.600000   \n",
       "25%       2014.0     2.000000     4.000000             10.250000   \n",
       "50%       2014.0     3.400000     6.000000             12.600000   \n",
       "75%       2014.0     4.300000     8.000000             15.550000   \n",
       "max       2014.0     8.400000    12.000000             30.200000   \n",
       "\n",
       "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
       "count          1067.000000           1067.000000               1067.000000   \n",
       "mean              9.474602             11.580881                 26.441425   \n",
       "std               2.794510              3.485595                  7.468702   \n",
       "min               4.900000              4.700000                 11.000000   \n",
       "25%               7.500000              9.000000                 21.000000   \n",
       "50%               8.800000             10.900000                 26.000000   \n",
       "75%              10.850000             13.350000                 31.000000   \n",
       "max              20.500000             25.800000                 60.000000   \n",
       "\n",
       "       CO2EMISSIONS  \n",
       "count   1067.000000  \n",
       "mean     256.228679  \n",
       "std       63.372304  \n",
       "min      108.000000  \n",
       "25%      207.000000  \n",
       "50%      251.000000  \n",
       "75%      294.000000  \n",
       "max      488.000000  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "842ee76e-e7f8-4f09-8e60-ef43dec26d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'ENGINESIZE'}>,\n",
       "        <Axes: title={'center': 'CYLINDERS'}>],\n",
       "       [<Axes: title={'center': 'FUELCONSUMPTION_COMB'}>,\n",
       "        <Axes: title={'center': 'CO2EMISSIONS'}>]], dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df[[\"ENGINESIZE\", \"CYLINDERS\", \"FUELCONSUMPTION_COMB\", \"CO2EMISSIONS\"]]\n",
    "df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "6a571750-43a1-49f1-9d8f-44f6980399d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.FUELCONSUMPTION_COMB, df.CO2EMISSIONS)\n",
    "plt.ylabel(\"CO2EMISSIONS\")\n",
    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "a8084633-7a27-491a-afcd-afce54138882",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.ENGINESIZE, df.CO2EMISSIONS)\n",
    "plt.xlabel(\"CO2EMISSIONS\")\n",
    "plt.xlabel(\"ENGINESIZE\")\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252c1dba-be0b-424f-beb2-18e62a603890",
   "metadata": {},
   "source": [
    "### Data separation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "6dc40bc3-fc3e-4f59-839a-a8ac26cf680f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1067, 3)\n",
      "(1067,)\n"
     ]
    }
   ],
   "source": [
    "x = df[[\"ENGINESIZE\", \"CYLINDERS\", \"FUELCONSUMPTION_COMB\"]].values\n",
    "y = df[\"CO2EMISSIONS\"].values\n",
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "e89516e6-6ea8-4b7e-8d34-57e15a559c65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(214, 3) (214,)\n",
      "(853, 3) (853,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=4)\n",
    "print(x_test.shape, y_test.shape)\n",
    "print(x_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6214df56-d9fe-4f06-ab9f-8a33be9700ff",
   "metadata": {},
   "source": [
    "### multiple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "dfb3ae6e-e78e-4bcf-88e7-f5a2529d8c7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.24537129  7.64355532  9.68132732] 65.25787570246109\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(x_train, y_train)\n",
    "print(regr.coef_, regr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f398cb91-868c-471d-b155-58c6c83253e9",
   "metadata": {},
   "source": [
    "## Evalution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "90c1b1f3-9c54-48c1-ad11-8162deead8bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.890023090970219"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "yhat = regr.predict(x_test)\n",
    "r2_score(y_test, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "5494d7f5-b9b9-4dc5-ac93-89e1a522824c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.92, 'Multiple Linear Regression co2 emissions')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "\n",
    "# Create the plot\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Define the independent variables\n",
    "x = df['ENGINESIZE']\n",
    "y = df['CYLINDERS']\n",
    "z = df['FUELCONSUMPTION_COMB']\n",
    "cm = plt.get_cmap(\"RdYlGn\")\n",
    "col = [cm(float(i)/(len(x))) for i in range(len(x))]\n",
    "\n",
    "# Add the data points\n",
    "ax.scatter(x, y, z, c=col, marker='o')\n",
    "\n",
    "# Fit a plane using np.linalg.lstsq\n",
    "A = np.vstack([x, y, np.ones_like(x)]).T\n",
    "plane_coef, _, _, _ = np.linalg.lstsq(A, z, rcond=None)\n",
    "\n",
    "# Create a meshgrid for the plane\n",
    "x_plane, y_plane = np.meshgrid(x, y)\n",
    "z_plane = plane_coef[0] * x_plane + plane_coef[1] * y_plane + plane_coef[2]\n",
    "\n",
    "# Add the regression plane\n",
    "ax.plot_surface(x_plane, y_plane, z_plane, alpha=0.5)\n",
    "\n",
    "# Add labels and title\n",
    "ax.set_xlabel('ENGINESIZE')\n",
    "ax.set_ylabel('CYLINDERS')\n",
    "ax.set_zlabel('FUELCONSUMPTION_COMB')\n",
    "plt.title('Multiple Linear Regression co2 emissions')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c779b97-b926-4663-a3a8-970cb07175a9",
   "metadata": {},
   "source": [
    "## Linear regression with only one variable \n",
    "### Data separation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "e2d8381f-562a-46bb-9e0d-08b627ca1260",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[\"ENGINESIZE\"].values\n",
    "y = df[\"CO2EMISSIONS\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "6d3975aa-6ffd-4342-ad22-d10018016e52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(214, 1) (214, 1)\n",
      "(853, 1) (853, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=4)\n",
    "xtrain = xtrain.reshape(-1, 1)\n",
    "ytrain = ytrain.reshape(-1, 1)\n",
    "xtest = xtest.reshape(-1, 1)\n",
    "ytest = ytest.reshape(-1, 1)\n",
    "print(xtest.shape, ytest.shape)\n",
    "print(xtrain.shape, ytrain.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "1278834c-a449-41d7-a93e-b386e7c73cb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\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-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3\" 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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "regr_ = linear_model.LinearRegression()\n",
    "fregr_.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b32468-a6cc-402f-942a-8dbbfecaff16",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "15a66ef0-55ac-40f0-a3f5-44418568b2a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8149387318398211"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "yhat = regr_.predict(xtest)\n",
    "r2_score(ytest, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "dcb0ecdd-e002-45d6-927b-46eea1493cb8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x162808fb0>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.ENGINESIZE, df.CO2EMISSIONS, label=\"co2\")\n",
    "plt.xlabel(\"CO2EMISSIONS\")\n",
    "plt.xlabel(\"ENGINESIZE\")\n",
    "plt.plot(xtest,(regr_.coef_[0]* xtest) + regr_.intercept_ , '-r',label=\"prediction\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea7e3188-fac1-43af-b9c6-b0009358903f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}