FoodDelivery_TimePrediction (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "951abd1b-ef41-4f6d-87bc-13f48ccc0dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://www.kaggle.com/datasets/gauravmalik26/food-delivery-dataset\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import statistics\n",
    "from geopy.distance import geodesic\n",
    "\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.preprocessing import LabelEncoder,StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import xgboost as XGBRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score,mean_absolute_error\n",
    "\n",
    "#ignore the warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a459e0ce-8f68-48a1-a2c9-b9aa62256696",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4d258f0f-460c-46c5-a8b3-81165a01b1ca",
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Delivery_person_ID</th>\n",
       "      <th>Delivery_person_Age</th>\n",
       "      <th>Delivery_person_Ratings</th>\n",
       "      <th>Restaurant_latitude</th>\n",
       "      <th>Restaurant_longitude</th>\n",
       "      <th>Delivery_location_latitude</th>\n",
       "      <th>Delivery_location_longitude</th>\n",
       "      <th>Order_Date</th>\n",
       "      <th>Time_Orderd</th>\n",
       "      <th>Time_Order_picked</th>\n",
       "      <th>Weatherconditions</th>\n",
       "      <th>Road_traffic_density</th>\n",
       "      <th>Vehicle_condition</th>\n",
       "      <th>Type_of_order</th>\n",
       "      <th>Type_of_vehicle</th>\n",
       "      <th>multiple_deliveries</th>\n",
       "      <th>Festival</th>\n",
       "      <th>City</th>\n",
       "      <th>Time_taken(min)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0x4607</td>\n",
       "      <td>INDORES13DEL02</td>\n",
       "      <td>37</td>\n",
       "      <td>4.9</td>\n",
       "      <td>22.745049</td>\n",
       "      <td>75.892471</td>\n",
       "      <td>22.765049</td>\n",
       "      <td>75.912471</td>\n",
       "      <td>19-03-2022</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>11:45:00</td>\n",
       "      <td>conditions Sunny</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>Snack</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Urban</td>\n",
       "      <td>(min) 24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0xb379</td>\n",
       "      <td>BANGRES18DEL02</td>\n",
       "      <td>34</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12.913041</td>\n",
       "      <td>77.683237</td>\n",
       "      <td>13.043041</td>\n",
       "      <td>77.813237</td>\n",
       "      <td>25-03-2022</td>\n",
       "      <td>19:45:00</td>\n",
       "      <td>19:50:00</td>\n",
       "      <td>conditions Stormy</td>\n",
       "      <td>Jam</td>\n",
       "      <td>2</td>\n",
       "      <td>Snack</td>\n",
       "      <td>scooter</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Metropolitian</td>\n",
       "      <td>(min) 33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0x5d6d</td>\n",
       "      <td>BANGRES19DEL01</td>\n",
       "      <td>23</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12.914264</td>\n",
       "      <td>77.678400</td>\n",
       "      <td>12.924264</td>\n",
       "      <td>77.688400</td>\n",
       "      <td>19-03-2022</td>\n",
       "      <td>08:30:00</td>\n",
       "      <td>08:45:00</td>\n",
       "      <td>conditions Sandstorms</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>Drinks</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Urban</td>\n",
       "      <td>(min) 26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0x7a6a</td>\n",
       "      <td>COIMBRES13DEL02</td>\n",
       "      <td>38</td>\n",
       "      <td>4.7</td>\n",
       "      <td>11.003669</td>\n",
       "      <td>76.976494</td>\n",
       "      <td>11.053669</td>\n",
       "      <td>77.026494</td>\n",
       "      <td>05-04-2022</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>18:10:00</td>\n",
       "      <td>conditions Sunny</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Metropolitian</td>\n",
       "      <td>(min) 21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0x70a2</td>\n",
       "      <td>CHENRES12DEL01</td>\n",
       "      <td>32</td>\n",
       "      <td>4.6</td>\n",
       "      <td>12.972793</td>\n",
       "      <td>80.249982</td>\n",
       "      <td>13.012793</td>\n",
       "      <td>80.289982</td>\n",
       "      <td>26-03-2022</td>\n",
       "      <td>13:30:00</td>\n",
       "      <td>13:45:00</td>\n",
       "      <td>conditions Cloudy</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>Snack</td>\n",
       "      <td>scooter</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Metropolitian</td>\n",
       "      <td>(min) 30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID Delivery_person_ID Delivery_person_Age Delivery_person_Ratings  \\\n",
       "0  0x4607     INDORES13DEL02                   37                     4.9   \n",
       "1  0xb379     BANGRES18DEL02                   34                     4.5   \n",
       "2  0x5d6d     BANGRES19DEL01                   23                     4.4   \n",
       "3  0x7a6a    COIMBRES13DEL02                   38                     4.7   \n",
       "4  0x70a2     CHENRES12DEL01                   32                     4.6   \n",
       "\n",
       "   Restaurant_latitude  Restaurant_longitude  Delivery_location_latitude  \\\n",
       "0            22.745049             75.892471                   22.765049   \n",
       "1            12.913041             77.683237                   13.043041   \n",
       "2            12.914264             77.678400                   12.924264   \n",
       "3            11.003669             76.976494                   11.053669   \n",
       "4            12.972793             80.249982                   13.012793   \n",
       "\n",
       "   Delivery_location_longitude  Order_Date Time_Orderd Time_Order_picked  \\\n",
       "0                    75.912471  19-03-2022    11:30:00          11:45:00   \n",
       "1                    77.813237  25-03-2022    19:45:00          19:50:00   \n",
       "2                    77.688400  19-03-2022    08:30:00          08:45:00   \n",
       "3                    77.026494  05-04-2022    18:00:00          18:10:00   \n",
       "4                    80.289982  26-03-2022    13:30:00          13:45:00   \n",
       "\n",
       "       Weatherconditions Road_traffic_density  Vehicle_condition  \\\n",
       "0       conditions Sunny                High                   2   \n",
       "1      conditions Stormy                 Jam                   2   \n",
       "2  conditions Sandstorms                 Low                   0   \n",
       "3       conditions Sunny              Medium                   0   \n",
       "4      conditions Cloudy                High                   1   \n",
       "\n",
       "  Type_of_order Type_of_vehicle multiple_deliveries Festival            City  \\\n",
       "0        Snack      motorcycle                    0      No           Urban    \n",
       "1        Snack         scooter                    1      No   Metropolitian    \n",
       "2       Drinks      motorcycle                    1      No           Urban    \n",
       "3       Buffet      motorcycle                    1      No   Metropolitian    \n",
       "4        Snack         scooter                    1      No   Metropolitian    \n",
       "\n",
       "  Time_taken(min)  \n",
       "0        (min) 24  \n",
       "1        (min) 33  \n",
       "2        (min) 26  \n",
       "3        (min) 21  \n",
       "4        (min) 30  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
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   "cell_type": "code",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45593 entries, 0 to 45592\n",
      "Data columns (total 20 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   ID                           45593 non-null  object \n",
      " 1   Delivery_person_ID           45593 non-null  object \n",
      " 2   Delivery_person_Age          45593 non-null  object \n",
      " 3   Delivery_person_Ratings      45593 non-null  object \n",
      " 4   Restaurant_latitude          45593 non-null  float64\n",
      " 5   Restaurant_longitude         45593 non-null  float64\n",
      " 6   Delivery_location_latitude   45593 non-null  float64\n",
      " 7   Delivery_location_longitude  45593 non-null  float64\n",
      " 8   Order_Date                   45593 non-null  object \n",
      " 9   Time_Orderd                  45593 non-null  object \n",
      " 10  Time_Order_picked            45593 non-null  object \n",
      " 11  Weatherconditions            45593 non-null  object \n",
      " 12  Road_traffic_density         45593 non-null  object \n",
      " 13  Vehicle_condition            45593 non-null  int64  \n",
      " 14  Type_of_order                45593 non-null  object \n",
      " 15  Type_of_vehicle              45593 non-null  object \n",
      " 16  multiple_deliveries          45593 non-null  object \n",
      " 17  Festival                     45593 non-null  object \n",
      " 18  City                         45593 non-null  object \n",
      " 19  Time_taken(min)              45593 non-null  object \n",
      "dtypes: float64(4), int64(1), object(15)\n",
      "memory usage: 7.0+ MB\n"
     ]
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   "source": [
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   ]
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   "cell_type": "code",
   "execution_count": 5,
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       "      <td>17.017729</td>\n",
       "      <td>8.185109</td>\n",
       "      <td>-30.905562</td>\n",
       "      <td>12.933284</td>\n",
       "      <td>18.546947</td>\n",
       "      <td>22.728163</td>\n",
       "      <td>30.914057</td>\n",
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       "      <th>Restaurant_longitude</th>\n",
       "      <td>45593.0</td>\n",
       "      <td>70.231332</td>\n",
       "      <td>22.883647</td>\n",
       "      <td>-88.366217</td>\n",
       "      <td>73.170000</td>\n",
       "      <td>75.898497</td>\n",
       "      <td>78.044095</td>\n",
       "      <td>88.433452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delivery_location_latitude</th>\n",
       "      <td>45593.0</td>\n",
       "      <td>17.465186</td>\n",
       "      <td>7.335122</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>12.988453</td>\n",
       "      <td>18.633934</td>\n",
       "      <td>22.785049</td>\n",
       "      <td>31.054057</td>\n",
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       "    <tr>\n",
       "      <th>Delivery_location_longitude</th>\n",
       "      <td>45593.0</td>\n",
       "      <td>70.845702</td>\n",
       "      <td>21.118812</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>73.280000</td>\n",
       "      <td>76.002574</td>\n",
       "      <td>78.107044</td>\n",
       "      <td>88.563452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vehicle_condition</th>\n",
       "      <td>45593.0</td>\n",
       "      <td>1.023359</td>\n",
       "      <td>0.839065</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "                               count       mean        std        min  \\\n",
       "Restaurant_latitude          45593.0  17.017729   8.185109 -30.905562   \n",
       "Restaurant_longitude         45593.0  70.231332  22.883647 -88.366217   \n",
       "Delivery_location_latitude   45593.0  17.465186   7.335122   0.010000   \n",
       "Delivery_location_longitude  45593.0  70.845702  21.118812   0.010000   \n",
       "Vehicle_condition            45593.0   1.023359   0.839065   0.000000   \n",
       "\n",
       "                                   25%        50%        75%        max  \n",
       "Restaurant_latitude          12.933284  18.546947  22.728163  30.914057  \n",
       "Restaurant_longitude         73.170000  75.898497  78.044095  88.433452  \n",
       "Delivery_location_latitude   12.988453  18.633934  22.785049  31.054057  \n",
       "Delivery_location_longitude  73.280000  76.002574  78.107044  88.563452  \n",
       "Vehicle_condition             0.000000   1.000000   2.000000   3.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   "source": [
    "df_train.describe().T"
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       "      <td>1</td>\n",
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       "      <th>Delivery_person_ID</th>\n",
       "      <td>45593</td>\n",
       "      <td>1320</td>\n",
       "      <td>PUNERES01DEL01</td>\n",
       "      <td>67</td>\n",
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       "      <th>Delivery_person_Age</th>\n",
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       "      <td>23</td>\n",
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       "      <td>2262</td>\n",
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       "      <th>Delivery_person_Ratings</th>\n",
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       "      <td>7148</td>\n",
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       "    <tr>\n",
       "      <th>Order_Date</th>\n",
       "      <td>45593</td>\n",
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       "      <td>15-03-2022</td>\n",
       "      <td>1192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_Orderd</th>\n",
       "      <td>45593</td>\n",
       "      <td>177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1731</td>\n",
       "    </tr>\n",
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       "      <th>Time_Order_picked</th>\n",
       "      <td>45593</td>\n",
       "      <td>193</td>\n",
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       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weatherconditions</th>\n",
       "      <td>45593</td>\n",
       "      <td>7</td>\n",
       "      <td>conditions Fog</td>\n",
       "      <td>7654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Road_traffic_density</th>\n",
       "      <td>45593</td>\n",
       "      <td>5</td>\n",
       "      <td>Low</td>\n",
       "      <td>15477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type_of_order</th>\n",
       "      <td>45593</td>\n",
       "      <td>4</td>\n",
       "      <td>Snack</td>\n",
       "      <td>11533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type_of_vehicle</th>\n",
       "      <td>45593</td>\n",
       "      <td>4</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>26435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multiple_deliveries</th>\n",
       "      <td>45593</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>28159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Festival</th>\n",
       "      <td>45593</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>44469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <td>45593</td>\n",
       "      <td>4</td>\n",
       "      <td>Metropolitian</td>\n",
       "      <td>34093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_taken(min)</th>\n",
       "      <td>45593</td>\n",
       "      <td>45</td>\n",
       "      <td>(min) 26</td>\n",
       "      <td>2123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count unique              top   freq\n",
       "ID                       45593  45593          0x4607       1\n",
       "Delivery_person_ID       45593   1320  PUNERES01DEL01      67\n",
       "Delivery_person_Age      45593     23               35   2262\n",
       "Delivery_person_Ratings  45593     29              4.8   7148\n",
       "Order_Date               45593     44       15-03-2022   1192\n",
       "Time_Orderd              45593    177             NaN    1731\n",
       "Time_Order_picked        45593    193         21:30:00    496\n",
       "Weatherconditions        45593      7   conditions Fog   7654\n",
       "Road_traffic_density     45593      5             Low   15477\n",
       "Type_of_order            45593      4           Snack   11533\n",
       "Type_of_vehicle          45593      4      motorcycle   26435\n",
       "multiple_deliveries      45593      5                1  28159\n",
       "Festival                 45593      3              No   44469\n",
       "City                     45593      4   Metropolitian   34093\n",
       "Time_taken(min)          45593     45         (min) 26   2123"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summary Statistics for non-numerical columns\n",
    "df_train.describe(exclude=np.number).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf452666-5686-4f8b-8c8d-8a2cdb8fc575",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Format Column Names\n",
    "#Change name Weatherconditions to Weather_conditions\n",
    "df_train.rename(columns={'Weatherconditions': 'Weather_conditions'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "716700b9-0b71-4235-95c7-aeed3f134f92",
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>45593</td>\n",
       "      <td>45593</td>\n",
       "      <td>0x4607</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delivery_person_ID</th>\n",
       "      <td>45593</td>\n",
       "      <td>1320</td>\n",
       "      <td>PUNERES01DEL01</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delivery_person_Age</th>\n",
       "      <td>45593</td>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>2262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delivery_person_Ratings</th>\n",
       "      <td>45593</td>\n",
       "      <td>29</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order_Date</th>\n",
       "      <td>45593</td>\n",
       "      <td>44</td>\n",
       "      <td>15-03-2022</td>\n",
       "      <td>1192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_Orderd</th>\n",
       "      <td>45593</td>\n",
       "      <td>177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_Order_picked</th>\n",
       "      <td>45593</td>\n",
       "      <td>193</td>\n",
       "      <td>21:30:00</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weather_conditions</th>\n",
       "      <td>45593</td>\n",
       "      <td>7</td>\n",
       "      <td>conditions Fog</td>\n",
       "      <td>7654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Road_traffic_density</th>\n",
       "      <td>45593</td>\n",
       "      <td>5</td>\n",
       "      <td>Low</td>\n",
       "      <td>15477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type_of_order</th>\n",
       "      <td>45593</td>\n",
       "      <td>4</td>\n",
       "      <td>Snack</td>\n",
       "      <td>11533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type_of_vehicle</th>\n",
       "      <td>45593</td>\n",
       "      <td>4</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>26435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multiple_deliveries</th>\n",
       "      <td>45593</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>28159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Festival</th>\n",
       "      <td>45593</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>44469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <td>45593</td>\n",
       "      <td>4</td>\n",
       "      <td>Metropolitian</td>\n",
       "      <td>34093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_taken(min)</th>\n",
       "      <td>45593</td>\n",
       "      <td>45</td>\n",
       "      <td>(min) 26</td>\n",
       "      <td>2123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count unique              top   freq\n",
       "ID                       45593  45593          0x4607       1\n",
       "Delivery_person_ID       45593   1320  PUNERES01DEL01      67\n",
       "Delivery_person_Age      45593     23               35   2262\n",
       "Delivery_person_Ratings  45593     29              4.8   7148\n",
       "Order_Date               45593     44       15-03-2022   1192\n",
       "Time_Orderd              45593    177             NaN    1731\n",
       "Time_Order_picked        45593    193         21:30:00    496\n",
       "Weather_conditions       45593      7   conditions Fog   7654\n",
       "Road_traffic_density     45593      5             Low   15477\n",
       "Type_of_order            45593      4           Snack   11533\n",
       "Type_of_vehicle          45593      4      motorcycle   26435\n",
       "multiple_deliveries      45593      5                1  28159\n",
       "Festival                 45593      3              No   44469\n",
       "City                     45593      4   Metropolitian   34093\n",
       "Time_taken(min)          45593     45         (min) 26   2123"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe(exclude=np.number).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4392a5f2-ac5b-4ffd-b663-8d0d489ddccf",
   "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>Time_taken(min)</th>\n",
       "      <th>Weather_conditions</th>\n",
       "      <th>City_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>INDO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>Stormy</td>\n",
       "      <td>BANG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>Sandstorms</td>\n",
       "      <td>BANG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>COIMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>CHEN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time_taken(min) Weather_conditions City_code\n",
       "0               24              Sunny      INDO\n",
       "1               33             Stormy      BANG\n",
       "2               26         Sandstorms      BANG\n",
       "3               21              Sunny     COIMB\n",
       "4               30             Cloudy      CHEN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Extraction\n",
    "def extract_column_value(df):\n",
    "    \n",
    "    #Extract time and convert to int\n",
    "    df['Time_taken(min)'] = df['Time_taken(min)'].apply(lambda x: int(x.split(' ')[1].strip()))\n",
    "    \n",
    "    #Extract Weather conditions\n",
    "    df['Weather_conditions'] = df['Weather_conditions'].apply(lambda x: x.split(' ')[1].strip())\n",
    "    \n",
    "    #Extract city code from Delivery person ID\n",
    "    df['City_code']=df['Delivery_person_ID'].str.split(\"RES\", expand=True)[0]\n",
    "    \n",
    "extract_column_value(df_train)\n",
    "df_train[['Time_taken(min)','Weather_conditions','City_code']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5299fc3e-9e97-49f6-96da-fdc398563bc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Update DataTypes\n",
    "def update_datatype(df):\n",
    "    \n",
    "    #Update datatype from object to float\n",
    "    df['Delivery_person_Age'] = df['Delivery_person_Age'].astype('float64')\n",
    "    df['Delivery_person_Ratings'] = df['Delivery_person_Ratings'].astype('float64')\n",
    "    df['multiple_deliveries'] = df['multiple_deliveries'].astype('float64')\n",
    "    \n",
    "    #Update datatype from object to datetime\n",
    "    df['Order_Date']=pd.to_datetime(df['Order_Date'],format=\"%d-%m-%Y\")\n",
    "    \n",
    "update_datatype(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "938dd5b9-48ee-4562-b3b0-9e08fd03b2ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop Columns\n",
    "df_train.drop(['ID','Delivery_person_ID'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e871e3e-0e11-46cc-85a2-afdfff422968",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no duplicate value present\n"
     ]
    }
   ],
   "source": [
    "#Check for Duplicate Values\n",
    "if (len(df_train[df_train.duplicated()])>0):\n",
    "    print(\"There are Duplicate values present\")\n",
    "else:\n",
    "    print(\"There is no duplicate value present\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5a65754-9695-4fa8-98c3-55b205a57243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delivery_person_Ratings        1908\n",
       "Delivery_person_Age            1854\n",
       "Time_Orderd                    1731\n",
       "City                           1200\n",
       "multiple_deliveries             993\n",
       "Weather_conditions              616\n",
       "Road_traffic_density            601\n",
       "Festival                        228\n",
       "Order_Date                        0\n",
       "Delivery_location_longitude       0\n",
       "Time_Order_picked                 0\n",
       "Delivery_location_latitude        0\n",
       "Vehicle_condition                 0\n",
       "Type_of_order                     0\n",
       "Type_of_vehicle                   0\n",
       "Restaurant_longitude              0\n",
       "Restaurant_latitude               0\n",
       "Time_taken(min)                   0\n",
       "City_code                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Handle Missing Values\n",
    "\n",
    "#Replace NaN to np.nan\n",
    "df_train.replace('NaN', float(np.nan), regex=True,inplace=True)\n",
    "\n",
    "#Show count of NaN values in data\n",
    "df_train.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a7a9aed-36d7-451b-80a0-662e1582e621",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1500 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data Visualisation\n",
    "cols = ['Delivery_person_Age','Delivery_person_Ratings','Weather_conditions','Road_traffic_density','multiple_deliveries','Festival','City']\n",
    "num_plots = len(cols)\n",
    "num_rows = (num_plots // 2) + (num_plots % 2)\n",
    "\n",
    "#create subplots in a grid layout\n",
    "fig, axes = plt.subplots(num_rows, 2, figsize=(20,15))\n",
    "\n",
    "for i, column_name in enumerate(cols):\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "\n",
    "    ax = axes[row, col]\n",
    "    sns.countplot(data=df_train, x=column_name, order=df_train[column_name].value_counts().sort_index().index, ax=ax)\n",
    "\n",
    "    ax.set_xlabel(column_name)\n",
    "    ax.set_ylabel('No. of Orders')\n",
    "    ax.set_title(column_name)\n",
    "    ax.tick_params(axis='x', rotation=45)\n",
    "    \n",
    "if num_plots % 2 != 0:\n",
    "    fig.delaxes(axes[-1, -1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc66e8fa-f0c4-4edf-ab15-ccdb5bc6198d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delivery_person_Age               0\n",
       "Delivery_person_Ratings           0\n",
       "Restaurant_latitude               0\n",
       "Restaurant_longitude              0\n",
       "Delivery_location_latitude        0\n",
       "Delivery_location_longitude       0\n",
       "Order_Date                        0\n",
       "Time_Orderd                    1731\n",
       "Time_Order_picked                 0\n",
       "Weather_conditions              616\n",
       "Road_traffic_density              0\n",
       "Vehicle_condition                 0\n",
       "Type_of_order                     0\n",
       "Type_of_vehicle                   0\n",
       "multiple_deliveries               0\n",
       "Festival                          0\n",
       "City                              0\n",
       "Time_taken(min)                   0\n",
       "City_code                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Handle missing values\n",
    "def handle_null_values(df):\n",
    "\n",
    "    #filling missing values randomly\n",
    "    df_train['Delivery_person_Age'].fillna(np.random.choice(df['Delivery_person_Age']), inplace=True)\n",
    "    df_train['Weather_conditions'].fillna(np.random.choice(df['Weather_conditions']), inplace=True)\n",
    "    \n",
    "    #filling missing values with the mode (most frequent value)\n",
    "    df_train['City'].fillna(df['City'].mode()[0], inplace=True)\n",
    "    df_train['Festival'].fillna(df['Festival'].mode()[0], inplace=True)\n",
    "    df_train['multiple_deliveries'].fillna(df['multiple_deliveries'].mode()[0], inplace=True)\n",
    "    df_train['Road_traffic_density'].fillna(df['Road_traffic_density'].mode()[0], inplace=True)\n",
    "    \n",
    "    #filling missing values with the median\n",
    "    df_train['Delivery_person_Ratings'].fillna(df['Delivery_person_Ratings'].median(), inplace=True)\n",
    "    \n",
    "handle_null_values(df_train)\n",
    "df_train.isnull().sum()\n",
    "\n",
    "#for Time_Ordered column we need to convert it into datatime object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "545b9561-a9d7-4f8d-8998-541a95fe0788",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Engineering\n",
    "#generating new features from the Order_Date column\n",
    "def extract_date_features(data):\n",
    "    \n",
    "    data[\"day\"] = data.Order_Date.dt.day\n",
    "    data[\"month\"] = data.Order_Date.dt.month\n",
    "    data[\"quarter\"] = data.Order_Date.dt.quarter\n",
    "    data[\"year\"] = data.Order_Date.dt.year\n",
    "    data['day_of_week'] = data.Order_Date.dt.day_of_week.astype(int)\n",
    "    data[\"is_month_start\"] = data.Order_Date.dt.is_month_start.astype(int)\n",
    "    data[\"is_month_end\"] = data.Order_Date.dt.is_month_end.astype(int)\n",
    "    data[\"is_quarter_start\"] = data.Order_Date.dt.is_quarter_start.astype(int)\n",
    "    data[\"is_quarter_end\"] = data.Order_Date.dt.is_quarter_end.astype(int)\n",
    "    data[\"is_year_start\"] = data.Order_Date.dt.is_year_start.astype(int)\n",
    "    data[\"is_year_end\"] = data.Order_Date.dt.is_year_end.astype(int)\n",
    "    data['is_weekend'] = np.where(data['day_of_week'].isin([5,6]),1,0)\n",
    "\n",
    "extract_date_features(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ec5a1f3a-3dc1-41d9-b6c6-23909886d3d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create new feature of the difference between the Order time and pickup time\n",
    "def calculate_time_diff(df):\n",
    "    \n",
    "    # Find the difference between ordered time & pickup time\n",
    "    df['Time_Orderd'] = pd.to_timedelta(df['Time_Orderd'])\n",
    "    df['Time_Order_picked'] = pd.to_timedelta(df['Time_Order_picked'])\n",
    "    \n",
    "    df['Time_Order_picked_formatted'] = df['Order_Date'] + df['Time_Order_picked']\n",
    "    # Handle cases where pickup time is earlier than ordered time\n",
    "    df['Time_Order_picked_formatted'] = np.where(df['Time_Order_picked'] < df['Time_Orderd'],df['Time_Order_picked_formatted'] + pd.DateOffset(days=1),df['Time_Order_picked_formatted'])\n",
    "    \n",
    "    df['Time_Ordered_formatted'] = df['Order_Date'] + df['Time_Orderd']\n",
    "    \n",
    "    df['order_prepare_time'] = (df['Time_Order_picked_formatted'] - df['Time_Ordered_formatted']).dt.total_seconds() / 60\n",
    "\n",
    "    # Handle null values by filling with the median\n",
    "    df['order_prepare_time'].fillna(df['order_prepare_time'].median(), inplace=True)\n",
    "    \n",
    "calculate_time_diff(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "84400fa8-e719-4e9b-addf-ad04f82748b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop all the time & date related columns\n",
    "df_train.drop(['Time_Orderd', 'Time_Order_picked', 'Time_Ordered_formatted', 'Time_Order_picked_formatted', 'Order_Date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7cd9946e-ca52-43b8-9feb-9d78104a35bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Delivery_person_Age', 'Delivery_person_Ratings', 'Restaurant_latitude',\n",
      "       'Restaurant_longitude', 'Delivery_location_latitude',\n",
      "       'Delivery_location_longitude', 'Weather_conditions',\n",
      "       'Road_traffic_density', 'Vehicle_condition', 'Type_of_order',\n",
      "       'Type_of_vehicle', 'multiple_deliveries', 'Festival', 'City',\n",
      "       'Time_taken(min)', 'City_code', 'day', 'month', 'quarter', 'year',\n",
      "       'day_of_week', 'is_month_start', 'is_month_end', 'is_quarter_start',\n",
      "       'is_quarter_end', 'is_year_start', 'is_year_end', 'is_weekend',\n",
      "       'order_prepare_time'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "20f2725b-835c-418e-a2f4-182ec08d678d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate distance between restaurant location & delivery location\n",
    "def calculate_distance(df):\n",
    "    df['distance']=np.zeros(len(df))\n",
    "    restaurant_coordinates=df[['Restaurant_latitude','Restaurant_longitude']].to_numpy()\n",
    "    delivery_location_coordinates=df[['Delivery_location_latitude','Delivery_location_longitude']].to_numpy()\n",
    "    df['distance'] = np.array([geodesic(restaurant, delivery).km for restaurant, delivery in zip(restaurant_coordinates, delivery_location_coordinates)])\n",
    "    df['distance']= df['distance'].astype(\"int64\")\n",
    "\n",
    "    #or use this to extract digists\n",
    "    #df['distance']= df['distance'].astype(\"str\").str.extract('(\\d+)').astype(\"int64\")\n",
    "calculate_distance(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b3137d96-8a52-4722-a66e-f0e9fadbbd92",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Preprocessing\n",
    "#Categorical Feature Encoding\n",
    "def label_encoding(df):\n",
    "    categorical_columns = df.select_dtypes(include='object').columns\n",
    "    label_encoder = LabelEncoder()\n",
    "    df[categorical_columns] = df[categorical_columns].apply(lambda col: label_encoder.fit_transform(col))\n",
    "\n",
    "label_encoding(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6299dc07-2f8b-4961-99c2-c3aca4ad05de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split Training & Testing Data\n",
    "X = df_train.drop('Time_taken(min)', axis=1)  # Features\n",
    "y = df_train['Time_taken(min)']  # Target variable\n",
    "\n",
    "# Split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6dea34d9-f3c2-46a1-be26-3bb3b5355663",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardisation\n",
    "# Create a StandardScaler object\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the scaler on the training data\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# Perform standardization on the training data\n",
    "X_train = scaler.transform(X_train)\n",
    "\n",
    "# Perform standardization on the testing data\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1e06bd07-672e-4125-bbfe-897ee98dc861",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression:\n",
      "R2 scores: [0.41262793 0.41528685 0.41620653 0.42782706 0.42117124]\n",
      "Mean R2 score: 0.4186239211582219\n",
      "\n",
      "DecisionTreeRegressor:\n",
      "R2 scores: [0.63714924 0.64394162 0.64881042 0.64315773 0.65970828]\n",
      "Mean R2 score: 0.646553459601606\n",
      "\n",
      "RandomForestRegressor:\n",
      "R2 scores: [0.80994607 0.80867911 0.81245204 0.81149505 0.81438595]\n",
      "Mean R2 score: 0.8113916448385933\n",
      "\n",
      "XGBRegressor:\n",
      "R2 scores: [0.81049034 0.80918699 0.8121119  0.81020928 0.81636787]\n",
      "Mean R2 score: 0.8116732746188784\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Model Building\n",
    "import xgboost as xgb\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "models = [\n",
    "    LinearRegression(),\n",
    "    DecisionTreeRegressor(),\n",
    "    RandomForestRegressor(),\n",
    "    xgb.XGBRegressor(),\n",
    "]\n",
    "\n",
    "for model in models: \n",
    "    scores = cross_val_score(model, X_train, y_train, cv=5, scoring='r2')\n",
    "    \n",
    "    print(f\"{model.__class__.__name__}:\")\n",
    "    print(\"R2 scores:\", scores)\n",
    "    print(\"Mean R2 score:\", scores.mean())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5e8a0e2e-dd74-46ee-8292-89fb2984c9fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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=9, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=20, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>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=9, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=20, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "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=9, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=20, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#XGboost is the best one\n",
    "# Create a XGB regressor model\n",
    "model = xgb.XGBRegressor(n_estimators=20,max_depth=9)\n",
    "\n",
    "# Fit the model on the training data\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8ddd6865-1d61-467a-80c7-59038f9321e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 3.15\n",
      "Mean Squared Error (MSE): 15.72\n",
      "Root Mean Squared Error (RMSE): 3.96\n",
      "R-squared (R2) Score: 0.82\n"
     ]
    }
   ],
   "source": [
    "#Evaluate Model\n",
    "# Make predictions on the test data\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Absolute Error (MAE):\", round(mae,2))\n",
    "print(\"Mean Squared Error (MSE):\", round(mse,2))\n",
    "print(\"Root Mean Squared Error (RMSE):\", round(rmse,2))\n",
    "print(\"R-squared (R2) Score:\", round(r2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "31564c45-5d6d-4db7-8d7c-505449744c95",
   "metadata": {},
   "outputs": [],
   "source": [
    "#An R2 score of 0.82 implies that the model can explain approximately 82% of the variance in the food delivery time based on the provided features."
   ]
  }
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