model_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries:\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/module_5_auto.csv'\n",
    "df = pd.read_csv(filepath, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>...</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0  symboling  normalized-losses         make  \\\n",
       "0             0           0          3                122  alfa-romero   \n",
       "1             1           1          3                122  alfa-romero   \n",
       "2             2           2          1                122  alfa-romero   \n",
       "3             3           3          2                164         audi   \n",
       "4             4           4          2                164         audi   \n",
       "\n",
       "  aspiration num-of-doors   body-style drive-wheels engine-location  ...  \\\n",
       "0        std          two  convertible          rwd           front  ...   \n",
       "1        std          two  convertible          rwd           front  ...   \n",
       "2        std          two    hatchback          rwd           front  ...   \n",
       "3        std         four        sedan          fwd           front  ...   \n",
       "4        std         four        sedan          4wd           front  ...   \n",
       "\n",
       "   compression-ratio  horsepower  peak-rpm  city-mpg  highway-mpg    price  \\\n",
       "0                9.0       111.0    5000.0        21           27  13495.0   \n",
       "1                9.0       111.0    5000.0        21           27  16500.0   \n",
       "2                9.0       154.0    5000.0        19           26  16500.0   \n",
       "3               10.0       102.0    5500.0        24           30  13950.0   \n",
       "4                8.0       115.0    5500.0        18           22  17450.0   \n",
       "\n",
       "  city-L/100km  horsepower-binned diesel  gas  \n",
       "0    11.190476             Medium      0    1  \n",
       "1    11.190476             Medium      0    1  \n",
       "2    12.368421             Medium      0    1  \n",
       "3     9.791667             Medium      0    1  \n",
       "4    13.055556             Medium      0    1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>...</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>152</td>\n",
       "      <td>...</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.919444</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>109</td>\n",
       "      <td>...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.922222</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>136</td>\n",
       "      <td>...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0  symboling  normalized-losses  wheel-base  \\\n",
       "0             0           0          3                122        88.6   \n",
       "1             1           1          3                122        88.6   \n",
       "2             2           2          1                122        94.5   \n",
       "3             3           3          2                164        99.8   \n",
       "4             4           4          2                164        99.4   \n",
       "\n",
       "     length     width  height  curb-weight  engine-size  ...  stroke  \\\n",
       "0  0.811148  0.890278    48.8         2548          130  ...    2.68   \n",
       "1  0.811148  0.890278    48.8         2548          130  ...    2.68   \n",
       "2  0.822681  0.909722    52.4         2823          152  ...    3.47   \n",
       "3  0.848630  0.919444    54.3         2337          109  ...    3.40   \n",
       "4  0.848630  0.922222    54.3         2824          136  ...    3.40   \n",
       "\n",
       "   compression-ratio  horsepower  peak-rpm  city-mpg  highway-mpg    price  \\\n",
       "0                9.0       111.0    5000.0        21           27  13495.0   \n",
       "1                9.0       111.0    5000.0        21           27  16500.0   \n",
       "2                9.0       154.0    5000.0        19           26  16500.0   \n",
       "3               10.0       102.0    5500.0        24           30  13950.0   \n",
       "4                8.0       115.0    5500.0        18           22  17450.0   \n",
       "\n",
       "   city-L/100km  diesel  gas  \n",
       "0     11.190476       0    1  \n",
       "1     11.190476       0    1  \n",
       "2     12.368421       0    1  \n",
       "3      9.791667       0    1  \n",
       "4     13.055556       0    1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, let's only use numeric data:\n",
    "\n",
    "df=df._get_numeric_data()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>152</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.919444</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.922222</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses  wheel-base    length     width  height  \\\n",
       "0          3                122        88.6  0.811148  0.890278    48.8   \n",
       "1          3                122        88.6  0.811148  0.890278    48.8   \n",
       "2          1                122        94.5  0.822681  0.909722    52.4   \n",
       "3          2                164        99.8  0.848630  0.919444    54.3   \n",
       "4          2                164        99.4  0.848630  0.922222    54.3   \n",
       "\n",
       "   curb-weight  engine-size  bore  stroke  compression-ratio  horsepower  \\\n",
       "0         2548          130  3.47    2.68                9.0       111.0   \n",
       "1         2548          130  3.47    2.68                9.0       111.0   \n",
       "2         2823          152  2.68    3.47                9.0       154.0   \n",
       "3         2337          109  3.19    3.40               10.0       102.0   \n",
       "4         2824          136  3.19    3.40                8.0       115.0   \n",
       "\n",
       "   peak-rpm  city-mpg  highway-mpg    price  city-L/100km  diesel  gas  \n",
       "0    5000.0        21           27  13495.0     11.190476       0    1  \n",
       "1    5000.0        21           27  16500.0     11.190476       0    1  \n",
       "2    5000.0        19           26  16500.0     12.368421       0    1  \n",
       "3    5500.0        24           30  13950.0      9.791667       0    1  \n",
       "4    5500.0        18           22  17450.0     13.055556       0    1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove the columns 'Unnamed:0.1' and 'Unnamed:0' since they do not provide any value to the models.\n",
    "\n",
    "df.drop(['Unnamed: 0.1', 'Unnamed: 0'], axis=1, inplace=True)\n",
    "\n",
    "# Let's take a look at the updated DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries for plotting:\n",
    "\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions for Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DistributionPlot(RedFunction, BlueFunction, RedName, BlueName, Title):\n",
    "    width = 12\n",
    "    height = 10\n",
    "    plt.figure(figsize=(width, height))\n",
    "    \n",
    "    ax1 = sns.kdeplot(RedFunction, color=\"r\", label=RedName)\n",
    "    ax2 = sns.kdeplot(BlueFunction, color=\"b\", label=BlueName, ax=ax1)\n",
    "\n",
    "    plt.title(Title)\n",
    "    plt.xlabel('Price (in dollars)')\n",
    "    plt.ylabel('Proportion of Cars')\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PollyPlot(xtrain, xtest, y_train, y_test, lr,poly_transform):\n",
    "    width = 12\n",
    "    height = 10\n",
    "    plt.figure(figsize=(width, height))\n",
    "    \n",
    "    \n",
    "    #training data \n",
    "    #testing data \n",
    "    # lr:  linear regression object \n",
    "    #poly_transform:  polynomial transformation object \n",
    " \n",
    "    xmax=max([xtrain.values.max(), xtest.values.max()])\n",
    "\n",
    "    xmin=min([xtrain.values.min(), xtest.values.min()])\n",
    "\n",
    "    x=np.arange(xmin, xmax, 0.1)\n",
    "\n",
    "\n",
    "    plt.plot(xtrain, y_train, 'ro', label='Training Data')\n",
    "    plt.plot(xtest, y_test, 'go', label='Test Data')\n",
    "    plt.plot(x, lr.predict(poly_transform.fit_transform(x.reshape(-1, 1))), label='Predicted Function')\n",
    "    plt.ylim([-10000, 60000])\n",
    "    plt.ylabel('Price')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1: Training and Testing\n",
    "\n",
    "An important step in testing your model is to split your data into training and testing data. We will place the target data price in a separate dataframe y_data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop price data in dataframe x_data:\n",
    "\n",
    "x_data=df.drop('price',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples : 21\n",
      "number of training samples: 180\n"
     ]
    }
   ],
   "source": [
    "# Now, we randomly split our data into training and testing data using the function train_test_split.\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.10, random_state=1)\n",
    "\n",
    "\n",
    "print(\"number of test samples :\", x_test.shape[0])\n",
    "print(\"number of training samples:\",x_train.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test_size parameter sets the proportion of data that is split into the testing set. In the above, the testing set is 10% of the total dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples : 81\n",
      "number of training samples: 120\n"
     ]
    }
   ],
   "source": [
    "# Use the function \"train_test_split\" to split up the dataset such that 40% of the data samples will be utilized for testing. Set the parameter \"random_state\" equal to zero.\n",
    "\n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x_data, y_data, test_size=0.4, random_state=0) \n",
    "print(\"number of test samples :\", x_test1.shape[0])\n",
    "print(\"number of training samples:\",x_train1.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's import LinearRegression from the module linear_model.\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "\n",
    "# We create a Linear Regression object:\n",
    "\n",
    "lre=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We fit the model using the feature \"horsepower\":\n",
    "\n",
    "lre.fit(x_train[['horsepower']], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36358755750788263"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's calculate the R^2 on the test data:\n",
    "\n",
    "lre.score(x_test[['horsepower']], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6619724197515104"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lre.score(x_train[['horsepower']], y_train)\n",
    "\n",
    "# We can see the R^2 is much smaller using the test data compared to the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7139364665406973"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the R^2 on the test data using 40% of the dataset for testing.\n",
    "\n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x_data, y_data, test_size=0.4, random_state=0)\n",
    "lre.fit(x_train1[['horsepower']],y_train1)\n",
    "lre.score(x_test1[['horsepower']],y_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes you do not have sufficient testing data; as a result, we may want to perform cross-validation. Let's go over several methods that we can use for cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Validation Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's import cross_val_score from the module model_selection.\n",
    "\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We input the object, the feature (\"horsepower\"), and the target data (y_data). The parameter 'cv' determines the number of folds. In this case, it is 4.\n",
    "\n",
    "Rcross = cross_val_score(lre, x_data[['horsepower']], y_data, cv=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.7746232 , 0.51716687, 0.74785353, 0.04839605])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The default scoring is R^2. Each element in the array has the average R^2 value for the fold:\n",
    "\n",
    "Rcross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the folds are 0.522009915042119 and the standard deviation is 0.2911839444756029\n"
     ]
    }
   ],
   "source": [
    "# We can calculate the average and standard deviation of our estimate:\n",
    "\n",
    "print(\"The mean of the folds are\", Rcross.mean(), \"and the standard deviation is\" , Rcross.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20254142.84026702, 43745493.2650517 , 12539630.34014931,\n",
       "       17561927.7224759 ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can use negative squared error as a score by setting the parameter 'scoring' metric to 'neg_mean_squared_error'.\n",
    "\n",
    "-1 * cross_val_score(lre,x_data[['horsepower']], y_data,cv=4,scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.5166761697127429)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average R^2 using two folds, then find the average R^2 for the second fold utilizing the \"horsepower\" feature:\n",
    "\n",
    "Rc=cross_val_score(lre,x_data[['horsepower']], y_data,cv=2)\n",
    "Rc.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can also use the function 'cross_val_predict' to predict the output. The function splits up the data into the specified number of folds, with one fold for testing and the other folds are used for training. First, import the function:\n",
    "\n",
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14141.63807508, 14141.63807508, 20814.29423473, 12745.03562306,\n",
       "       14762.35027598])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We input the object, the feature \"horsepower\", and the target data y_data. The parameter 'cv' determines the number of folds. In this case, it is 4. We can produce an output:\n",
    "\n",
    "yhat = cross_val_predict(lre,x_data[['horsepower']], y_data,cv=4)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2: Overfitting, Underfitting and Model Selection\n",
    "\n",
    "It turns out that the test data, sometimes referred to as the \"out of sample data\", is a much better measure of how well your model performs in the real world. One reason for this is overfitting.\n",
    "\n",
    "Let's go over some examples. It turns out these differences are more apparent in Multiple Linear Regression and Polynomial Regression so we will explore overfitting in that context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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/1.5/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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's create Multiple Linear Regression objects and train the model using 'horsepower', 'curb-weight', 'engine-size' and 'highway-mpg' as features\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7426.6731551 , 28323.75090803, 14213.38819709,  4052.34146983,\n",
       "       34500.19124244])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction using training data:\n",
    "\n",
    "yhat_train = lr.predict(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "yhat_train[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11349.35089149,  5884.11059106, 11208.6928275 ,  6641.07786278,\n",
       "       15565.79920282])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction using test data:\n",
    "\n",
    "yhat_test = lr.predict(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "yhat_test[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's perform some model evaluation using our training and testing data separately. First, we import the seaborn and matplotlib library for plotting.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's examine the distribution of the predicted values of the training data.\n",
    "\n",
    "Title = 'Distribution  Plot of  Predicted Value Using Training Data vs Training Data Distribution'\n",
    "DistributionPlot(y_train, yhat_train, \"Actual Values (Train)\", \"Predicted Values (Train)\", Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 1: Plot of predicted values using the training data compared to the actual values of the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Title='Distribution  Plot of  Predicted Value Using Test Data vs Data Distribution of Test Data'\n",
    "DistributionPlot(y_test,yhat_test,\"Actual Values (Test)\",\"Predicted Values (Test)\",Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 2: Plot of predicted value using the test data compared to the actual values of the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overfitting\n",
    "\n",
    "Overfitting occurs when the model fits the noise, but not the underlying process. Therefore, when testing your model using the test set, your model does not perform as well since it is modelling noise, not the underlying process that generated the relationship. Let's create a degree 5 polynomial model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's use 55 percent of the data for training and the rest for testing:\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.45, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>PolynomialFeatures(degree=5)</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;PolynomialFeatures<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PolynomialFeatures(degree=5)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "PolynomialFeatures(degree=5)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will perform a degree 5 polynomial transformation on the feature 'horsepower'.\n",
    "\n",
    "pr = PolynomialFeatures(degree=5)\n",
    "x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "x_test_pr = pr.fit_transform(x_test[['horsepower']])\n",
    "pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4\" 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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" 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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's create a Linear Regression model \"poly\" and train it.\n",
    "\n",
    "poly = LinearRegression()\n",
    "poly.fit(x_train_pr, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6728.65566037,  7307.9878638 , 12213.7877412 , 18893.24796457,\n",
       "       19995.95185894])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can see the output of our model using the method \"predict.\" We assign the values to \"yhat\".\n",
    "\n",
    "yhat = poly.predict(x_test_pr)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values: [ 6728.65566037  7307.9878638  12213.7877412  18893.24796457]\n",
      "True values: [ 6295. 10698. 13860. 13499.]\n"
     ]
    }
   ],
   "source": [
    "# Let's take the first five predicted values and compare it to the actual targets.\n",
    "\n",
    "print(\"Predicted values:\", yhat[0:4])\n",
    "print(\"True values:\", y_test[0:4].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will use the function \"PollyPlot\" that we defined at the beginning of the lab to display the training data, testing data, and the predicted function.\n",
    "\n",
    "PollyPlot(x_train['horsepower'], x_test['horsepower'], y_train, y_test, poly,pr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 3: A polynomial regression model where red dots represent training data, green dots represent test data, and the blue line represents the model prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5567716902121724"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R^2 of the training data:\n",
    "\n",
    "poly.score(x_train_pr, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-29.87134054062532"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R^2 of the test data:\n",
    "\n",
    "poly.score(x_test_pr, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the R^2 for the training data is 0.5567 while the R^2 on the test data was -29.87. The lower the R^2, the worse the model. A negative R^2 is a sign of overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(3, 0.75, 'Maximum R^2 ')"
      ]
     },
     "execution_count": 41,
     "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": [
    "# Let's see how the R^2 changes on the test data for different order polynomials and then plot the results:\n",
    "\n",
    "Rsqu_test = []\n",
    "\n",
    "order = [1, 2, 3, 4]\n",
    "for n in order:\n",
    "    pr = PolynomialFeatures(degree=n)\n",
    "    \n",
    "    x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "    \n",
    "    x_test_pr = pr.fit_transform(x_test[['horsepower']])    \n",
    "    \n",
    "    lr.fit(x_train_pr, y_train)\n",
    "    \n",
    "    Rsqu_test.append(lr.score(x_test_pr, y_test))\n",
    "\n",
    "plt.plot(order, Rsqu_test)\n",
    "plt.xlabel('order')\n",
    "plt.ylabel('R^2')\n",
    "plt.title('R^2 Using Test Data')\n",
    "plt.text(3, 0.75, 'Maximum R^2 ')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following function will be used in the next section. Please run the cell below.\n",
    "\n",
    "def f(order, test_data):\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=test_data, random_state=0)\n",
    "    pr = PolynomialFeatures(degree=order)\n",
    "    x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "    x_test_pr = pr.fit_transform(x_test[['horsepower']])\n",
    "    poly = LinearRegression()\n",
    "    poly.fit(x_train_pr,y_train)\n",
    "    PollyPlot(x_train['horsepower'], x_test['horsepower'], y_train, y_test, poly,pr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3d725c895094c54b32eae3406e3b670",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=3, description='order', max=6), FloatSlider(value=0.45, description='tes…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.f(order, test_data)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The following interface allows you to experiment with different polynomial orders and different amounts of data.\n",
    "\n",
    "interact(f, order=(0, 6, 1), test_data=(0.05, 0.95, 0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can perform polynomial transformations with more than one feature. Create a \"PolynomialFeatures\" object \"pr1\" of degree two.\n",
    "\n",
    "pr1=PolynomialFeatures(degree=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the training and testing samples for the features 'horsepower', 'curb-weight', 'engine-size' and 'highway-mpg'. Hint: use the method \"fit_transform\".\n",
    "\n",
    "x_train_pr1=pr1.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "\n",
    "x_test_pr1=pr1.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110, 15)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many dimensions does the new feature have? Hint: use the attribute \"shape\".\n",
    "\n",
    "x_train_pr1.shape #there are now 15 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a linear regression model \"poly1\". Train the object using the method \"fit\" using the polynomial features.\n",
    "\n",
    "poly1=LinearRegression().fit(x_train_pr1,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the method \"predict\" to predict an output on the polynomial features, then use the function \"DistributionPlot\" to display the distribution of the predicted test output vs. the actual test data.\n",
    "\n",
    "yhat_test1=poly1.predict(x_test_pr1)\n",
    "\n",
    "Title='Distribution  Plot of  Predicted Value Using Test Data vs Data Distribution of Test Data'\n",
    "\n",
    "DistributionPlot(y_test, yhat_test1, \"Actual Values (Test)\", \"Predicted Values (Test)\", Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted value is higher than actual value for cars where the price $10,000 range, conversely the predicted price is lower than the price cost in the $30,000 to $40,000 range. As such the model is not as accurate in these ranges."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 3: Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's perform a degree two polynomial transformation on our data.\n",
    "\n",
    "pr=PolynomialFeatures(degree=2)\n",
    "x_train_pr=pr.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])\n",
    "x_test_pr=pr.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's import Ridge from the module linear models.\n",
    "\n",
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create a Ridge regression object, setting the regularization parameter (alpha) to 0.1\n",
    "\n",
    "RigeModel=Ridge(alpha=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\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-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=1)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=1)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Like regular regression, you can fit the model using the method fit.\n",
    "\n",
    "RigeModel.fit(x_train_pr, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Similarly, you can obtain a prediction:\n",
    "\n",
    "yhat = RigeModel.predict(x_test_pr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: [ 6570.82441941  9636.24891471 20949.92322737 19403.60313256]\n",
      "test set : [ 6295. 10698. 13860. 13499.]\n"
     ]
    }
   ],
   "source": [
    "# Let's compare the first five predicted samples to our test set:\n",
    "\n",
    "print('predicted:', yhat[0:4])\n",
    "print('test set :', y_test[0:4].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:07<00:00, 135.43it/s, Test Score=0.564, Train Score=0.859]\n"
     ]
    }
   ],
   "source": [
    "# We select the value of alpha that minimizes the test error. To do so, we can use a for loop. We have also created a progress bar to see how many iterations we have completed so far.\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "Rsqu_test = []\n",
    "Rsqu_train = []\n",
    "dummy1 = []\n",
    "Alpha = 10 * np.array(range(0,1000))\n",
    "pbar = tqdm(Alpha)\n",
    "\n",
    "for alpha in pbar:\n",
    "    RigeModel = Ridge(alpha=alpha) \n",
    "    RigeModel.fit(x_train_pr, y_train)\n",
    "    test_score, train_score = RigeModel.score(x_test_pr, y_test), RigeModel.score(x_train_pr, y_train)\n",
    "    \n",
    "    pbar.set_postfix({\"Test Score\": test_score, \"Train Score\": train_score})\n",
    "\n",
    "    Rsqu_test.append(test_score)\n",
    "    Rsqu_train.append(train_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23c2c381e50>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can plot out the value of R^2 for different alphas:\n",
    "\n",
    "width = 12\n",
    "height = 10\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "plt.plot(Alpha,Rsqu_test, label='validation data  ')\n",
    "plt.plot(Alpha,Rsqu_train, 'r', label='training Data ')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('R^2')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 4: The blue line represents the R^2 of the validation data, and the red line represents the R^2 of the training data. The x-axis represents the different values of Alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the model is built and tested on the same data, so the training and test data are the same.\n",
    "\n",
    "The red line in Figure 4 represents the R^2 of the training data. As alpha increases the R^2 decreases. Therefore, as alpha increases, the model performs worse on the training data\n",
    "\n",
    "The blue line represents the R^2 on the validation data. As the value for alpha increases, the R^2 increases and converges at a point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5418576440206506"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform Ridge regression. Calculate the R^2 using the polynomial features, use the training data to train the model and use the test data to test the model. The parameter alpha should be set to 10.\n",
    "\n",
    "RigeModel = Ridge(alpha=10) \n",
    "RigeModel.fit(x_train_pr, y_train)\n",
    "RigeModel.score(x_test_pr, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 4: Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'alpha': [0.001, 0.1, 1, 10, 100, 1000, 10000, 100000, 100000]}]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We create a dictionary of parameter values:\n",
    "\n",
    "parameters1= [{'alpha': [0.001,0.1,1, 10, 100, 1000, 10000, 100000, 100000]}]\n",
    "parameters1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\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-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 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-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Ridge()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Ridge regression object:\n",
    "\n",
    "RR=Ridge()\n",
    "RR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a ridge grid search object:\n",
    "\n",
    "Grid1 = GridSearchCV(RR, parameters1,cv=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\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-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7 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-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 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-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7 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-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 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-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=4, estimator=Ridge(),\n",
       "             param_grid=[{&#x27;alpha&#x27;: [0.001, 0.1, 1, 10, 100, 1000, 10000, 100000,\n",
       "                                    100000]}])</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=4, estimator=Ridge(),\n",
       "             param_grid=[{&#x27;alpha&#x27;: [0.001, 0.1, 1, 10, 100, 1000, 10000, 100000,\n",
       "                                    100000]}])</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Ridge</label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=10000)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=10000)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=4, estimator=Ridge(),\n",
       "             param_grid=[{'alpha': [0.001, 0.1, 1, 10, 100, 1000, 10000, 100000,\n",
       "                                    100000]}])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model:\n",
    "\n",
    "Grid1.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\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-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8 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-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 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-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8 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-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 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-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=10000)</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-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=10000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=10000)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The object finds the best parameter values on the validation data. We can obtain the estimator with the best parameters and assign it to the variable BestRR as follows:\n",
    "\n",
    "BestRR=Grid1.best_estimator_\n",
    "BestRR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8411649831036152"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We now test our model on the test data:\n",
    "\n",
    "BestRR.score(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {\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-9 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 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-9 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-9 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-9 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-9 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-9 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-9 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 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-9 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-9 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-9 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-9 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-9 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-9 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 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-9 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 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-9 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-9 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-9 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-9 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-9 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-9 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-9 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-9 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-9 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-9 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-9 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-9 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-9 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-9 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 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-9 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-9 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-9 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-9 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-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=10000)</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-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=10000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=10000)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform a grid search for the alpha parameter and the normalization parameter, then find the best values of the parameters:\n",
    "\n",
    "parameters2 = [{'alpha': [0.001, 0.1, 1, 10, 100, 1000, 10000, 100000, 100000]}]\n",
    "\n",
    "Grid2 = GridSearchCV(Ridge(), parameters2, cv=4)\n",
    "Grid2.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_data)\n",
    "best_alpha = Grid2.best_params_['alpha']\n",
    "best_ridge_model = Ridge(alpha=best_alpha)\n",
    "best_ridge_model.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}