Waiter’s_tip_prediction_using_Machine_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c91c3df1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Downloading xgboost-2.0.2-py3-none-win_amd64.whl (99.8 MB)\n",
      "     ---------------------------------------- 99.8/99.8 MB 4.0 MB/s eta 0:00:00\n",
      "Requirement already satisfied: numpy in d:\\users\\rishav\\anaconda3\\lib\\site-packages (from xgboost) (1.21.5)\n",
      "Requirement already satisfied: scipy in d:\\users\\rishav\\anaconda3\\lib\\site-packages (from xgboost) (1.9.1)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-2.0.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "81ab2100",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sb \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from sklearn.metrics import mean_absolute_error as mae \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder \n",
    "from sklearn.linear_model import LinearRegression \n",
    "from xgboost import XGBRegressor \n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor \n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08242323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('tips.csv') \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be1e53a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "96c91fa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   total_bill  244 non-null    float64\n",
      " 1   tip         244 non-null    float64\n",
      " 2   sex         244 non-null    object \n",
      " 3   smoker      244 non-null    object \n",
      " 4   day         244 non-null    object \n",
      " 5   time        244 non-null    object \n",
      " 6   size        244 non-null    int64  \n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 13.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49d85d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_bill</th>\n",
       "      <td>244.0</td>\n",
       "      <td>19.785943</td>\n",
       "      <td>8.902412</td>\n",
       "      <td>3.07</td>\n",
       "      <td>13.3475</td>\n",
       "      <td>17.795</td>\n",
       "      <td>24.1275</td>\n",
       "      <td>50.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip</th>\n",
       "      <td>244.0</td>\n",
       "      <td>2.998279</td>\n",
       "      <td>1.383638</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.900</td>\n",
       "      <td>3.5625</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>244.0</td>\n",
       "      <td>2.569672</td>\n",
       "      <td>0.951100</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count       mean       std   min      25%     50%      75%    max\n",
       "total_bill  244.0  19.785943  8.902412  3.07  13.3475  17.795  24.1275  50.81\n",
       "tip         244.0   2.998279  1.383638  1.00   2.0000   2.900   3.5625  10.00\n",
       "size        244.0   2.569672  0.951100  1.00   2.0000   2.000   3.0000   6.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f637c69c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bill    0\n",
       "tip           0\n",
       "sex           0\n",
       "smoker        0\n",
       "day           0\n",
       "time          0\n",
       "size          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2aecb22b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15,8)) \n",
    "\n",
    "for i, col in enumerate(['total_bill', 'tip']): \n",
    "    plt.subplot(2,3, i + 1) \n",
    "    sb.distplot(df[col]) \n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "15cbc50f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15,8))\n",
    "\n",
    "for i, col in enumerate(['total_bill', 'tip']):\n",
    "    plt.subplot(2,3,i+1)\n",
    "    sb.boxplot(df[col])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "46be7f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((244, 7), (238, 7))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape, df[(df['total_bill']<45) & (df['tip']<7)].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79811645",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['total_bill']<45) & (df['tip']<7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5f9d0351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat = df.loc[:,'sex':'size'].columns \n",
    "\n",
    "plt.subplots(figsize=(15,8)) \n",
    "for i, col in enumerate(feat): \n",
    "    plt.subplot(2,3, i + 1) \n",
    "    sb.countplot(df[col]) \n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd40d0ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['total_bill'], df['tip']) \n",
    "plt.title('Total Bill v/s Total Tip') \n",
    "plt.xlabel('Total Bill') \n",
    "plt.ylabel('Total Tip') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de79504f",
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.242500</td>\n",
       "      <td>1.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.448013</td>\n",
       "      <td>2.582308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.899722</td>\n",
       "      <td>3.206667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.137647</td>\n",
       "      <td>3.814706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30.068000</td>\n",
       "      <td>4.028000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.383333</td>\n",
       "      <td>5.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      total_bill       tip\n",
       "size                      \n",
       "1       7.242500  1.437500\n",
       "2      16.448013  2.582308\n",
       "3      21.899722  3.206667\n",
       "4      27.137647  3.814706\n",
       "5      30.068000  4.028000\n",
       "6      30.383333  5.300000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['size']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7346e4d4",
   "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>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dinner</th>\n",
       "      <td>19.882059</td>\n",
       "      <td>2.966235</td>\n",
       "      <td>2.582353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lunch</th>\n",
       "      <td>17.168676</td>\n",
       "      <td>2.728088</td>\n",
       "      <td>2.411765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_bill       tip      size\n",
       "time                                  \n",
       "Dinner   19.882059  2.966235  2.582353\n",
       "Lunch    17.168676  2.728088  2.411765"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['time']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "873df2c1",
   "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>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>17.151579</td>\n",
       "      <td>2.734737</td>\n",
       "      <td>2.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>19.175542</td>\n",
       "      <td>2.736024</td>\n",
       "      <td>2.457831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>20.724865</td>\n",
       "      <td>3.228243</td>\n",
       "      <td>2.797297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>17.682742</td>\n",
       "      <td>2.771452</td>\n",
       "      <td>2.451613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      total_bill       tip      size\n",
       "day                                 \n",
       "Fri    17.151579  2.734737  2.105263\n",
       "Sat    19.175542  2.736024  2.457831\n",
       "Sun    20.724865  3.228243  2.797297\n",
       "Thur   17.682742  2.771452  2.451613"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['day']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "664f36ee",
   "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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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    0       0    2     0     2\n",
       "1       10.34  1.66    1       0    2     0     3\n",
       "2       21.01  3.50    1       0    2     0     3\n",
       "3       23.68  3.31    1       0    2     0     2\n",
       "4       24.59  3.61    0       0    2     0     4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder() \n",
    "\n",
    "for col in df.columns: \n",
    "    if df[col].dtype == object: \n",
    "        df[col] = le.fit_transform(df[col]) \n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6a947861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7)) \n",
    "sb.heatmap(df.corr() > 0.7, annot = True, cbar = False) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4534873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((190, 6), (48, 6))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df.drop('tip', axis=1) \n",
    "target = df['tip'] \n",
    "\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(features, target, test_size=0.2, random_state=22) \n",
    "X_train.shape, X_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1bc8df67",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler() \n",
    "X_train = scaler.fit_transform(X_train) \n",
    "X_val = scaler.transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "73711bd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression() : \n",
      "Training Accuracy :  0.6866406322422778\n",
      "Validation Accuracy :  0.7903774704017298\n",
      "\n",
      "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
      "             colsample_bylevel=None, colsample_bynode=None,\n",
      "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
      "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "             gamma=None, grow_policy=None, importance_type=None,\n",
      "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
      "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
      "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
      "             num_parallel_tree=None, random_state=None, ...) : \n",
      "Training Accuracy :  0.02728007923929315\n",
      "Validation Accuracy :  0.7682166453202566\n",
      "\n",
      "RandomForestRegressor() : \n",
      "Training Accuracy :  0.2890421052631578\n",
      "Validation Accuracy :  0.7234479166666663\n",
      "\n",
      "AdaBoostRegressor() : \n",
      "Training Accuracy :  0.6203958327355088\n",
      "Validation Accuracy :  0.80364242176696\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = [LinearRegression(), XGBRegressor(), RandomForestRegressor(), AdaBoostRegressor()] \n",
    "\n",
    "for i in range(4): \n",
    "    models[i].fit(X_train, Y_train) \n",
    "\n",
    "    print(f'{models[i]} : ') \n",
    "    pred_train = models[i].predict(X_train) \n",
    "    print('Training Accuracy : ', mae(Y_train, pred_train)) \n",
    "\n",
    "    pred_val = models[i].predict(X_val) \n",
    "    print('Validation Accuracy : ', mae(Y_val, pred_val)) \n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccb24eaf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}