Assg-Community/ML - Assignment 4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "465bf151",
   "metadata": {},
   "source": [
    "# Import necessary libraries¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5723ecea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fccfbc5a",
   "metadata": {},
   "source": [
    "# Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a03ba8b",
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"C:\\Users\\athir\\data_sets\\ML\\Assg_community\\tips.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b9cf08b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12e174d7",
   "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>total_bill</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill     sex smoker  day    time  size   tip\n",
       "0       16.99  Female     No  Sun  Dinner     2  1.01\n",
       "1       10.34    Male     No  Sun  Dinner     3  1.66\n",
       "2       21.01    Male     No  Sun  Dinner     3  3.50\n",
       "3       23.68    Male     No  Sun  Dinner     2  3.31\n",
       "4       24.59  Female     No  Sun  Dinner     4  3.61"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[['total_bill', 'sex', 'smoker', 'day', 'time', 'size','tip']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41de5d62",
   "metadata": {},
   "source": [
    "### data cleaning, data wrangling, data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7ed6555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bill    0\n",
       "sex           0\n",
       "smoker        0\n",
       "day           0\n",
       "time          0\n",
       "size          0\n",
       "tip           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05646ac",
   "metadata": {},
   "source": [
    "# Create a Numerical pipeline for preprocessing all the numerical variables present in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0c984b9",
   "metadata": {},
   "source": [
    "## step 1: Import all the libraries and functins needed for data preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "acaa3457",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# missing value treatment\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "sim_imp = SimpleImputer(strategy = 'median')\n",
    "sim_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d378853",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature scaling\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mms = MinMaxScaler()\n",
    "mms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b3dd026",
   "metadata": {},
   "source": [
    "## step 2: Mention the steps needed to perform automation and store it in the 'steps' variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "774fb37e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Missing value treatment', SimpleImputer(strategy='median')),\n",
       " ('Feature scaling', MinMaxScaler())]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_steps = [('Missing value treatment',sim_imp),('Feature scaling',mms)]\n",
    "num_steps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38537018",
   "metadata": {},
   "source": [
    "## step 3: Import pipeline function from sklearn and create pipeline with the \"steps\" variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03563a6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;Missing value treatment&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                (&#x27;Feature scaling&#x27;, MinMaxScaler())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;Missing value treatment&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                (&#x27;Feature scaling&#x27;, MinMaxScaler())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('Missing value treatment', SimpleImputer(strategy='median')),\n",
       "                ('Feature scaling', MinMaxScaler())])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "num_pipe = Pipeline(num_steps)\n",
    "num_pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2f7d6d54",
   "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>total_bill</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.291579</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.152283</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.375786</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.431713</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.450775</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill     sex smoker  day    time  size   tip\n",
       "0    0.291579  Female     No  Sun  Dinner   0.2  1.01\n",
       "1    0.152283    Male     No  Sun  Dinner   0.4  1.66\n",
       "2    0.375786    Male     No  Sun  Dinner   0.4  3.50\n",
       "3    0.431713    Male     No  Sun  Dinner   0.2  3.31\n",
       "4    0.450775  Female     No  Sun  Dinner   0.6  3.61"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['total_bill','size']] = num_pipe.fit_transform(data[['total_bill','size']])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "285b0367",
   "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>total_bill</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.291579</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.152283</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.375786</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.431713</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.450775</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill     sex  smoker  day    time  size   tip\n",
       "0    0.291579  Female       0  Sun  Dinner   0.2  1.01\n",
       "1    0.152283    Male       0  Sun  Dinner   0.4  1.66\n",
       "2    0.375786    Male       0  Sun  Dinner   0.4  3.50\n",
       "3    0.431713    Male       0  Sun  Dinner   0.2  3.31\n",
       "4    0.450775  Female       0  Sun  Dinner   0.6  3.61"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {'No':0,'Yes':1}\n",
    "data['smoker'] = data['smoker'].replace(dic)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "935b1304",
   "metadata": {},
   "source": [
    "# Create a Categorical pipeline for preprocessing all the categorical variables in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7bd72f6",
   "metadata": {},
   "source": [
    "## step 1: Import all the libraries and functins needed for data preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "341a89d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</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\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SimpleImputer(strategy='most_frequent')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "si = SimpleImputer(strategy = 'most_frequent')\n",
    "si"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "450541ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder()</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneHotEncoder()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature encoding\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ohe = OneHotEncoder()\n",
    "ohe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b73355ac",
   "metadata": {},
   "source": [
    "## step 2: Mention the steps needed to perform automation and store it in the 'steps' variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e11d7bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Missing value treatment', SimpleImputer(strategy='most_frequent')),\n",
       " ('Feature encoding', OneHotEncoder())]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_steps = [('Missing value treatment',si),('Feature encoding',ohe)]\n",
    "cat_steps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5ad10d1",
   "metadata": {},
   "source": [
    "## step 3: Import pipeline function from sklearn and create pipeline with the \"steps\" variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4f3533f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;Missing value treatment&#x27;,\n",
       "                 SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                (&#x27;Feature encoding&#x27;, OneHotEncoder())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;Missing value treatment&#x27;,\n",
       "                 SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                (&#x27;Feature encoding&#x27;, OneHotEncoder())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('Missing value treatment',\n",
       "                 SimpleImputer(strategy='most_frequent')),\n",
       "                ('Feature encoding', OneHotEncoder())])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "cat_pipe = Pipeline(cat_steps)\n",
    "cat_pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9ca3e0b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<244x2 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 244 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ohe = cat_pipe.fit_transform(data[['sex']])\n",
    "data_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "32c77026",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ohe.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "88c3679e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Female  Male\n",
       "0       1.0   0.0\n",
       "1       0.0   1.0\n",
       "2       0.0   1.0\n",
       "3       0.0   1.0\n",
       "4       1.0   0.0\n",
       "..      ...   ...\n",
       "239     0.0   1.0\n",
       "240     1.0   0.0\n",
       "241     0.0   1.0\n",
       "242     0.0   1.0\n",
       "243     1.0   0.0\n",
       "\n",
       "[244 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ohe = pd.DataFrame(data_ohe.toarray(),columns = ['Female','Male'])\n",
    "data_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e41c7ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<244x4 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 244 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1_ohe = cat_pipe.fit_transform(data[['day']])\n",
    "data1_ohe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "101fdd8d",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1_ohe.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "41c931aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.291579</td>\n",
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       "      <td>0.152283</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>Dinner</td>\n",
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       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>0.505027</td>\n",
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       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>0.410557</td>\n",
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       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.00</td>\n",
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       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0.308965</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0.329074</td>\n",
       "      <td>Female</td>\n",
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       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "     total_bill     sex  smoker   day    time  size   tip\n",
       "0      0.291579  Female       0   Sun  Dinner   0.2  1.01\n",
       "1      0.152283    Male       0   Sun  Dinner   0.4  1.66\n",
       "2      0.375786    Male       0   Sun  Dinner   0.4  3.50\n",
       "3      0.431713    Male       0   Sun  Dinner   0.2  3.31\n",
       "4      0.450775  Female       0   Sun  Dinner   0.6  3.61\n",
       "..          ...     ...     ...   ...     ...   ...   ...\n",
       "239    0.543779    Male       0   Sat  Dinner   0.4  5.92\n",
       "240    0.505027  Female       1   Sat  Dinner   0.2  2.00\n",
       "241    0.410557    Male       1   Sat  Dinner   0.2  2.00\n",
       "242    0.308965    Male       0   Sat  Dinner   0.2  1.75\n",
       "243    0.329074  Female       0  Thur  Dinner   0.2  3.00\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
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   "execution_count": 22,
   "id": "623cd37e",
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   "outputs": [
    {
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      "text/plain": [
       "array(['Sun', 'Sat', 'Thur', 'Fri'], dtype=object)"
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     "execution_count": 22,
     "metadata": {},
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   "execution_count": 23,
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       "     Fri  Sat  Sun  Thurs\n",
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     "metadata": {},
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    "data1_ohe = pd.DataFrame(data1_ohe.toarray(), columns = ['Fri','Sat','Sun','Thurs'])\n",
    "data1_ohe"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "59fbf83b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<244x2 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 244 stored elements in Compressed Sparse Row format>"
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     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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    "data2_ohe = cat_pipe.fit_transform(data[['time']])\n",
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   "id": "55e69f63",
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   "cell_type": "code",
   "execution_count": 26,
   "id": "22e51cd1",
   "metadata": {},
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       "     Dinner  Lunch\n",
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       "      <td>0.375786</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.431713</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.450775</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill     sex  smoker  day    time  size   tip  Female  Male  Fri  \\\n",
       "0    0.291579  Female       0  Sun  Dinner   0.2  1.01     1.0   0.0  0.0   \n",
       "1    0.152283    Male       0  Sun  Dinner   0.4  1.66     0.0   1.0  0.0   \n",
       "2    0.375786    Male       0  Sun  Dinner   0.4  3.50     0.0   1.0  0.0   \n",
       "3    0.431713    Male       0  Sun  Dinner   0.2  3.31     0.0   1.0  0.0   \n",
       "4    0.450775  Female       0  Sun  Dinner   0.6  3.61     1.0   0.0  0.0   \n",
       "\n",
       "   Sat  Sun  Thurs  Dinner  Lunch  \n",
       "0  0.0  1.0    0.0     1.0    0.0  \n",
       "1  0.0  1.0    0.0     1.0    0.0  \n",
       "2  0.0  1.0    0.0     1.0    0.0  \n",
       "3  0.0  1.0    0.0     1.0    0.0  \n",
       "4  0.0  1.0    0.0     1.0    0.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([data,data_ohe,data1_ohe,data2_ohe], axis =1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7158778c",
   "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>total_bill</th>\n",
       "      <th>smoker</th>\n",
       "      <th>size</th>\n",
       "      <th>tip</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "      <th>Thurs</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.291579</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.152283</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.375786</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.431713</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.450775</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill  smoker  size   tip  Female  Male  Fri  Sat  Sun  Thurs  Dinner  \\\n",
       "0    0.291579       0   0.2  1.01     1.0   0.0  0.0  0.0  1.0    0.0     1.0   \n",
       "1    0.152283       0   0.4  1.66     0.0   1.0  0.0  0.0  1.0    0.0     1.0   \n",
       "2    0.375786       0   0.4  3.50     0.0   1.0  0.0  0.0  1.0    0.0     1.0   \n",
       "3    0.431713       0   0.2  3.31     0.0   1.0  0.0  0.0  1.0    0.0     1.0   \n",
       "4    0.450775       0   0.6  3.61     1.0   0.0  0.0  0.0  1.0    0.0     1.0   \n",
       "\n",
       "   Lunch  \n",
       "0    0.0  \n",
       "1    0.0  \n",
       "2    0.0  \n",
       "3    0.0  \n",
       "4    0.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['sex','day','time'],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d841121",
   "metadata": {},
   "source": [
    "# separate the data into x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c7a3a327",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop('tip',axis=1)\n",
    "y = data['tip']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "423ea752",
   "metadata": {},
   "source": [
    "# split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e1a3cc90",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af86c958",
   "metadata": {},
   "source": [
    "# Perform 10 Fold Cross Validation on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0b0a2dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KFold(n_splits=10, random_state=None, shuffle=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits = 10)\n",
    "kf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5d0210f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object _BaseKFold.split at 0x00000215522700B0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf.split(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ce6bdf61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(array([ 25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,\n",
       "          38,  39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,\n",
       "          51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,\n",
       "          64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,\n",
       "          77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,\n",
       "          90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,\n",
       "         103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,\n",
       "         116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,\n",
       "         129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,\n",
       "         142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,\n",
       "         155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "         168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,\n",
       "         181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,\n",
       "         194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,\n",
       "         207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
       "         220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,\n",
       "         233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243]),\n",
       "  array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "         17, 18, 19, 20, 21, 22, 23, 24])),\n",
       " (array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "          13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  50,\n",
       "          51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,\n",
       "          64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,\n",
       "          77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,\n",
       "          90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,\n",
       "         103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,\n",
       "         116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,\n",
       "         129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,\n",
       "         142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,\n",
       "         155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "         168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,\n",
       "         181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,\n",
       "         194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,\n",
       "         207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
       "         220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,\n",
       "         233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243]),\n",
       "  array([25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,\n",
       "         42, 43, 44, 45, 46, 47, 48, 49])),\n",
       " (array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "          13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "          26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "          39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  75,  76,\n",
       "          77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,\n",
       "          90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,\n",
       "         103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,\n",
       "         116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,\n",
       "         129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,\n",
       "         142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,\n",
       "         155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "         168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,\n",
       "         181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,\n",
       "         194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,\n",
       "         207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
       "         220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,\n",
       "         233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243]),\n",
       "  array([50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,\n",
       "         67, 68, 69, 70, 71, 72, 73, 74])),\n",
       " (array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "          13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "          26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "          39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "          52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "          65,  66,  67,  68,  69,  70,  71,  72,  73,  74, 100, 101, 102,\n",
       "         103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,\n",
       "         116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,\n",
       "         129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,\n",
       "         142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,\n",
       "         155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "         168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,\n",
       "         181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,\n",
       "         194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,\n",
       "         207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
       "         220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,\n",
       "         233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243]),\n",
       "  array([75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n",
       "         92, 93, 94, 95, 96, 97, 98, 99])),\n",
       " (array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "          13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "          26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "          39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "          52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "          65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "          78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "          91,  92,  93,  94,  95,  96,  97,  98,  99, 124, 125, 126, 127,\n",
       "         128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140,\n",
       "         141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "         154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166,\n",
       "         167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179,\n",
       "         180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192,\n",
       "         193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,\n",
       "         206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218,\n",
       "         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n",
       "         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243]),\n",
       "  array([100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,\n",
       "         113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123])),\n",
       " (array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "          13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "          26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "          39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "          52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "          65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "          78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "          91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "         104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "         117, 118, 119, 120, 121, 122, 123, 148, 149, 150, 151, 152, 153,\n",
       "         154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166,\n",
       "         167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179,\n",
       "         180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192,\n",
       "         193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,\n",
       "         206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218,\n",
       "         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n",
       "         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243]),\n",
       "  array([124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,\n",
       "         137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147])),\n",
       " (array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "          13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "          26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "          39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "          52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "          65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "          78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "          91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "         104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "         117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "         130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "         143, 144, 145, 146, 147, 172, 173, 174, 175, 176, 177, 178, 179,\n",
       "         180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192,\n",
       "         193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,\n",
       "         206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218,\n",
       "         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n",
       "         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243]),\n",
       "  array([148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160,\n",
       "         161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171])),\n",
       " (array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "          13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "          26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "          39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "          52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "          65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "          78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "          91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "         104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "         117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "         130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "         143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "         156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "         169, 170, 171, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,\n",
       "         206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218,\n",
       "         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n",
       "         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243]),\n",
       "  array([172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,\n",
       "         185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195])),\n",
       " (array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "          13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "          26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "          39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "          52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "          65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "          78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "          91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "         104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "         117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "         130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "         143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "         156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "         169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "         182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
       "         195, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n",
       "         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243]),\n",
       "  array([196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n",
       "         209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219])),\n",
       " (array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "          13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "          26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "          39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "          52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "          65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "          78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "          91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "         104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "         117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "         130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "         143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "         156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "         169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "         182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
       "         195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
       "         208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219]),\n",
       "  array([220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,\n",
       "         233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243]))]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(kf.split(x,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaa17b41",
   "metadata": {},
   "source": [
    "# Determine whether Linear Regression or KNeighborsRegressor suits better on the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb2aa52a",
   "metadata": {},
   "source": [
    "## Apply linear regression on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "68c658d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9a901fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f2ce1726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.75976768, 2.99712626, 2.81673204, 1.4632311 , 3.12654885,\n",
       "       1.77482578, 2.54679105, 3.11359077, 2.88681115, 4.50140326,\n",
       "       3.18428659, 3.17091975, 2.3527022 , 2.29224593, 2.87517409,\n",
       "       4.16944264, 1.92603957, 2.24308317, 2.3679374 , 3.30322842,\n",
       "       3.79547836, 2.84290839, 2.5417152 , 2.43574813, 2.32853543,\n",
       "       2.58333477, 2.76729999, 4.59978345, 3.73195325, 2.40199442,\n",
       "       2.31295548, 2.27962689, 2.46828159, 1.92661159, 2.71399481,\n",
       "       2.30185826, 2.71063603, 2.09555601, 5.60088215, 3.34295006,\n",
       "       2.25147162, 2.27534617, 2.56519997, 4.33905752, 2.11239476,\n",
       "       2.81768662, 2.60608807, 2.99566606, 2.72262204])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_lr = lr.predict(x_test)\n",
    "y_pred_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "366d7d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5545031052534936"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test,y_pred_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02a8b71a",
   "metadata": {},
   "source": [
    "## Apply KNeighborsRegressor on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ddc6c927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</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-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knr = KNeighborsRegressor()\n",
    "knr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9c652a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</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-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "58f78efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.61 , 3.356, 3.218, 2.544, 2.956, 3.41 , 3.014, 2.536, 3.3  ,\n",
       "       3.732, 3.6  , 2.494, 2.558, 3.286, 2.6  , 4.606, 1.576, 2.416,\n",
       "       3.216, 3.2  , 2.754, 2.544, 3.004, 2.558, 3.004, 3.286, 2.2  ,\n",
       "       3.456, 3.606, 2.544, 2.544, 2.012, 2.566, 2.496, 3.202, 2.426,\n",
       "       2.734, 1.576, 5.766, 2.228, 2.2  , 2.012, 2.378, 4.312, 2.496,\n",
       "       2.436, 2.858, 3.22 , 2.734])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_knr = knr.predict(x_test)\n",
    "y_pred_knr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0a11a4ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4565110890972297"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test,y_pred_knr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00bbda7f",
   "metadata": {},
   "source": [
    "- Linear regression is performing better on this data with 55.45% of accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd925e74",
   "metadata": {},
   "source": [
    "# Apply 10 Fold Cross Validation and find out the best accuracy using linear Regression Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f9139f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dcd6a599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.57370317,  0.48785305,  0.64562012,  0.47289651,  0.09956561,\n",
       "        0.64694796,  0.52541767, -0.80599458,  0.53212589,  0.05037821])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(lr,x,y,cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64365f42",
   "metadata": {},
   "source": [
    "- 6th train-test set is giving the best accuracy of 64.69%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea98e195",
   "metadata": {},
   "source": [
    "# Apply 10 Fold Cross Validation and find out the best accuracy using K Neighbors Regression Algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c2c2d301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</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-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knr = KNeighborsRegressor()\n",
    "knr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "51db19ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.6283269 ,  0.40282969,  0.74023892, -0.00532146, -0.00730875,\n",
       "        0.28413942, -0.03697643, -0.49128019,  0.5030174 , -0.15520576])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(knr,x,y,cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "606f2256",
   "metadata": {},
   "source": [
    "- 3rd train-test set is giving the best accuracy of 74.02%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc848c50",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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