Food-Delivery-Time-Prediction-Using-Machine-Learning.ipynb

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   "source": [
    "# Food-Delivery-Time-Prediction-Using-Machine-Learning\n",
    "\n",
    "\n",
    "![Food delivery image](https://img.freepik.com/vektoren-kostenlos/food-delivery-logo-mit-fahrradmann-oder-kurier_1308-48914.jpg?t=st=1725915722~exp=1725919322~hmac=6df2d925d04f73b50aa5f55fa69745d083f6e53ee6a72eb5d594ceb1999bc53a&w=996)\r\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Introduction\n",
    "In the food delivery industry, accurate and timely deliveries are crucial for maintaining customer satisfaction and optimizing operational efficiency. Predicting delivery times effectively helps businesses manage resources, minimize delays, and enhance the overall customer experience. To address these needs, we developed a predictive model that estimates delivery times by analyzing various factors affecting the process. By leveraging data-driven insights, this model aims to improve delivery accuracy and streamline operations..\n",
    "### Methodology\n",
    "To build the food delivery time prediction model, we followed a systematic approach:\n",
    "\n",
    " 1) Data Cleaning: The dataset was meticulously cleaned to remove errors, inconsistencies, and missing values, ensuring the reliability and accuracy of the data used in the model.\n",
    "\n",
    " 2) Feature Engineering: We derived meaningful insights from raw data by creating new features such as delivery person’s age, ratings, location coordinates, and time-related variables. These engineered features provided valuable information to enhance the model’s predictive capability.\n",
    "\n",
    " 3) Model Development: Several regression algorithms were used to build the model, including linear regression, decision trees, random forest, and XGBoost. The model was trained using cross-validation techniques to ensure robustness and prevent overfitting.\n",
    "\n",
    " 4) Model Evaluation: We evaluated the performance of the models using metrics such as mean squared error (MSE) and R-squared (R2) to determine accuracy and goodness-of-fit. This helped in selecting the best model for predicting delivery times.\n",
    "\n",
    " By combining these steps, we developed a robust prediction model that businesses can use to optimize their delivery operations.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b8ce06-77e2-4380-9fff-c7226cee5108",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "565db896-30fd-492e-891c-f0cc8503eb91",
   "metadata": {},
   "outputs": [],
   "source": [
    "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, GridSearchCV\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 xgb\n",
    "from sklearn.metrics import mean_squared_error, r2_score,mean_absolute_error\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ac35ac-92ec-4c7d-a49c-f933fc0a2892",
   "metadata": {},
   "source": [
    "## Load & Understand Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "58d73111-39b5-4f58-a5c0-6de9428087be",
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   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>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": 517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train=pd.read_csv(r\"C:\\Users\\UNIQUE\\Desktop\\Code Work\\Dataset\\train.csv\")\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "id": "687e5217-689b-4642-9be9-f77ccc17b531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Delivery_person_ID', 'Delivery_person_Age',\n",
       "       'Delivery_person_Ratings', 'Restaurant_latitude',\n",
       "       'Restaurant_longitude', 'Delivery_location_latitude',\n",
       "       'Delivery_location_longitude', 'Order_Date', 'Time_Orderd',\n",
       "       'Time_Order_picked', 'Weatherconditions', 'Road_traffic_density',\n",
       "       'Vehicle_condition', 'Type_of_order', 'Type_of_vehicle',\n",
       "       'multiple_deliveries', 'Festival', 'City', 'Time_taken(min)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "id": "58bda809-5127-47a6-a99a-7ad24eeabc69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Dataset: (45593, 20)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Dataset:\" , df_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "68099442-7f8b-41ae-ae40-faf1b0f4d9f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "3912d863-ee85-4723-97a0-907880ca035b",
   "metadata": {},
   "outputs": [
    {
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       "<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>Restaurant_latitude</th>\n",
       "      <td>45593.0</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               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": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check statistical values for fields with numerical datatype\n",
    "df_train.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "id": "80aac797-4f9f-4c5f-b63f-5312c46e8cd4",
   "metadata": {},
   "outputs": [
    {
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       "  <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>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": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check statistical values for fields with other than numerical datatype\n",
    "df_train.describe(exclude=np.number).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0026fb6f-d7ac-413e-bb58-7ca12ffd34ae",
   "metadata": {},
   "source": [
    "# Observations:\n",
    "\n",
    "1. There are null values present in Time_Orderd column.\n",
    "\n",
    "2. Data Formatting will be required for Weatherconditions & Time_taken(min) column.\n",
    "\n",
    "3. Both numerical & categorical features are present.\n",
    "\n",
    "4. ID & Delivery_person_ID will not be used to build the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "id": "119297f4-1bfc-411e-a3de-42d2e8740492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID\n",
      "ID\n",
      "0x4607     1\n",
      "0x1f3e     1\n",
      "0xe251     1\n",
      "0x3f31     1\n",
      "0x4a78     1\n",
      "          ..\n",
      "0xc3f1     1\n",
      "0x5db7     1\n",
      "0x1985     1\n",
      "0xceda     1\n",
      "0x5fb2     1\n",
      "Name: count, Length: 45593, dtype: int64\n",
      "------------------------------------\n",
      "Delivery_person_ID\n",
      "Delivery_person_ID\n",
      "PUNERES01DEL01     67\n",
      "JAPRES11DEL02      67\n",
      "HYDRES04DEL02      66\n",
      "JAPRES03DEL01      66\n",
      "VADRES11DEL02      66\n",
      "                   ..\n",
      "DEHRES18DEL03       7\n",
      "AURGRES11DEL03      7\n",
      "KOLRES09DEL03       6\n",
      "KOCRES16DEL03       6\n",
      "BHPRES010DEL03      5\n",
      "Name: count, Length: 1320, dtype: int64\n",
      "------------------------------------\n",
      "Delivery_person_Age\n",
      "Delivery_person_Age\n",
      "35      2262\n",
      "36      2260\n",
      "37      2227\n",
      "30      2226\n",
      "38      2219\n",
      "24      2210\n",
      "32      2202\n",
      "22      2196\n",
      "29      2191\n",
      "33      2187\n",
      "28      2179\n",
      "25      2174\n",
      "34      2166\n",
      "26      2159\n",
      "21      2153\n",
      "27      2150\n",
      "39      2144\n",
      "20      2136\n",
      "31      2120\n",
      "23      2087\n",
      "NaN     1854\n",
      "50        53\n",
      "15        38\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n",
      "Delivery_person_Ratings\n",
      "Delivery_person_Ratings\n",
      "4.8     7148\n",
      "4.7     7142\n",
      "4.9     7041\n",
      "4.6     6940\n",
      "5       3996\n",
      "4.5     3303\n",
      "NaN     1908\n",
      "4.1     1430\n",
      "4.2     1418\n",
      "4.3     1409\n",
      "4.4     1361\n",
      "4       1077\n",
      "3.5      249\n",
      "3.8      228\n",
      "3.7      225\n",
      "3.6      207\n",
      "3.9      197\n",
      "6         53\n",
      "1         38\n",
      "3.4       32\n",
      "3.1       29\n",
      "3.2       29\n",
      "3.3       25\n",
      "2.6       22\n",
      "2.7       22\n",
      "2.5       20\n",
      "2.8       19\n",
      "2.9       19\n",
      "3          6\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n",
      "Restaurant_latitude\n",
      "Restaurant_latitude\n",
      " 0.000000     3640\n",
      " 26.911378     182\n",
      " 26.914142     180\n",
      " 26.892312     176\n",
      " 26.902940     176\n",
      "              ... \n",
      "-23.355164       1\n",
      "-15.513150       1\n",
      "-22.311358       1\n",
      "-27.161661       1\n",
      "-12.978453       1\n",
      "Name: count, Length: 657, dtype: int64\n",
      "------------------------------------\n",
      "Restaurant_longitude\n",
      "Restaurant_longitude\n",
      " 0.000000     3640\n",
      " 75.789034     182\n",
      " 75.805704     181\n",
      " 75.793007     177\n",
      " 75.806896     176\n",
      "              ... \n",
      "-76.626167       1\n",
      "-85.316842       1\n",
      "-76.643622       1\n",
      "-72.814492       1\n",
      "-77.643685       1\n",
      "Name: count, Length: 518, dtype: int64\n",
      "------------------------------------\n",
      "Delivery_location_latitude\n",
      "Delivery_location_latitude\n",
      "0.130000     341\n",
      "0.020000     337\n",
      "0.090000     336\n",
      "0.060000     336\n",
      "0.070000     335\n",
      "            ... \n",
      "19.976969      1\n",
      "19.916219      1\n",
      "26.562001      1\n",
      "23.324249      1\n",
      "20.005337      1\n",
      "Name: count, Length: 4373, dtype: int64\n",
      "------------------------------------\n",
      "Delivery_location_longitude\n",
      "Delivery_location_longitude\n",
      "0.130000     341\n",
      "0.020000     337\n",
      "0.090000     336\n",
      "0.060000     336\n",
      "0.070000     335\n",
      "            ... \n",
      "75.428894      1\n",
      "75.386017      1\n",
      "80.444002      1\n",
      "77.524007      1\n",
      "75.446722      1\n",
      "Name: count, Length: 4373, dtype: int64\n",
      "------------------------------------\n",
      "Order_Date\n",
      "Order_Date\n",
      "15-03-2022    1192\n",
      "03-04-2022    1178\n",
      "13-03-2022    1169\n",
      "26-03-2022    1166\n",
      "24-03-2022    1162\n",
      "09-03-2022    1159\n",
      "05-04-2022    1157\n",
      "05-03-2022    1154\n",
      "07-03-2022    1153\n",
      "03-03-2022    1150\n",
      "19-03-2022    1150\n",
      "21-03-2022    1149\n",
      "11-03-2022    1149\n",
      "30-03-2022    1141\n",
      "01-03-2022    1140\n",
      "28-03-2022    1139\n",
      "17-03-2022    1134\n",
      "01-04-2022    1133\n",
      "02-03-2022    1012\n",
      "10-03-2022     996\n",
      "16-03-2022     995\n",
      "20-03-2022     994\n",
      "02-04-2022     992\n",
      "06-03-2022     986\n",
      "04-03-2022     981\n",
      "29-03-2022     977\n",
      "25-03-2022     975\n",
      "14-03-2022     974\n",
      "11-02-2022     970\n",
      "18-03-2022     968\n",
      "31-03-2022     967\n",
      "27-03-2022     965\n",
      "12-03-2022     964\n",
      "08-03-2022     964\n",
      "23-03-2022     964\n",
      "06-04-2022     961\n",
      "13-02-2022     957\n",
      "15-02-2022     945\n",
      "04-04-2022     941\n",
      "17-02-2022     939\n",
      "12-02-2022     864\n",
      "16-02-2022     861\n",
      "18-02-2022     855\n",
      "14-02-2022     851\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n",
      "Time_Orderd\n",
      "Time_Orderd\n",
      "NaN         1731\n",
      "21:55:00     461\n",
      "17:55:00     456\n",
      "20:00:00     449\n",
      "22:20:00     448\n",
      "            ... \n",
      "12:25:00      57\n",
      "14:15:00      56\n",
      "16:00:00      53\n",
      "13:20:00      52\n",
      "16:30:00      51\n",
      "Name: count, Length: 177, dtype: int64\n",
      "------------------------------------\n",
      "Time_Order_picked\n",
      "Time_Order_picked\n",
      "21:30:00    496\n",
      "22:50:00    474\n",
      "22:40:00    458\n",
      "18:40:00    457\n",
      "17:55:00    456\n",
      "           ... \n",
      "15:10:00     48\n",
      "16:15:00     46\n",
      "16:10:00     43\n",
      "17:10:00     39\n",
      "16:20:00     38\n",
      "Name: count, Length: 193, dtype: int64\n",
      "------------------------------------\n",
      "Weatherconditions\n",
      "Weatherconditions\n",
      "conditions Fog           7654\n",
      "conditions Stormy        7586\n",
      "conditions Cloudy        7536\n",
      "conditions Sandstorms    7495\n",
      "conditions Windy         7422\n",
      "conditions Sunny         7284\n",
      "conditions NaN            616\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n",
      "Road_traffic_density\n",
      "Road_traffic_density\n",
      "Low        15477\n",
      "Jam        14143\n",
      "Medium     10947\n",
      "High        4425\n",
      "NaN          601\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n",
      "Vehicle_condition\n",
      "Vehicle_condition\n",
      "2    15034\n",
      "1    15030\n",
      "0    15009\n",
      "3      520\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n",
      "Type_of_order\n",
      "Type_of_order\n",
      "Snack      11533\n",
      "Meal       11458\n",
      "Drinks     11322\n",
      "Buffet     11280\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n",
      "Type_of_vehicle\n",
      "Type_of_vehicle\n",
      "motorcycle           26435\n",
      "scooter              15276\n",
      "electric_scooter      3814\n",
      "bicycle                 68\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n",
      "multiple_deliveries\n",
      "multiple_deliveries\n",
      "1       28159\n",
      "0       14095\n",
      "2        1985\n",
      "NaN       993\n",
      "3         361\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n",
      "Festival\n",
      "Festival\n",
      "No      44469\n",
      "Yes       896\n",
      "NaN       228\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n",
      "City\n",
      "City\n",
      "Metropolitian     34093\n",
      "Urban             10136\n",
      "NaN                1200\n",
      "Semi-Urban          164\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n",
      "Time_taken(min)\n",
      "Time_taken(min)\n",
      "(min) 26    2123\n",
      "(min) 25    2050\n",
      "(min) 27    1976\n",
      "(min) 28    1965\n",
      "(min) 29    1956\n",
      "(min) 19    1824\n",
      "(min) 15    1810\n",
      "(min) 18    1765\n",
      "(min) 16    1706\n",
      "(min) 17    1696\n",
      "(min) 24    1680\n",
      "(min) 23    1643\n",
      "(min) 20    1640\n",
      "(min) 22    1626\n",
      "(min) 21    1601\n",
      "(min) 33    1259\n",
      "(min) 30    1218\n",
      "(min) 31    1213\n",
      "(min) 34    1172\n",
      "(min) 32    1124\n",
      "(min) 38     887\n",
      "(min) 36     852\n",
      "(min) 39     847\n",
      "(min) 35     832\n",
      "(min) 37     828\n",
      "(min) 11     757\n",
      "(min) 10     750\n",
      "(min) 12     746\n",
      "(min) 14     739\n",
      "(min) 13     716\n",
      "(min) 43     567\n",
      "(min) 42     561\n",
      "(min) 40     555\n",
      "(min) 41     553\n",
      "(min) 44     553\n",
      "(min) 47     295\n",
      "(min) 49     280\n",
      "(min) 48     277\n",
      "(min) 46     274\n",
      "(min) 45     241\n",
      "(min) 53     100\n",
      "(min) 51      94\n",
      "(min) 54      91\n",
      "(min) 52      79\n",
      "(min) 50      72\n",
      "Name: count, dtype: int64\n",
      "------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Explore each column\n",
    "for column in df_train.columns:\n",
    "    print(column)\n",
    "    print(df_train[column].value_counts())\n",
    "    print(\"------------------------------------\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "718cb81d-9518-47e4-9c77-a0371fb27801",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "049545af-40e5-4715-9dab-1a64edfa228e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ID', 'Delivery_person_ID', 'Delivery_person_Age',\n",
      "       'Delivery_person_Ratings', 'Restaurant_latitude',\n",
      "       'Restaurant_longitude', 'Delivery_location_latitude',\n",
      "       'Delivery_location_longitude', 'Order_Date', 'Time_Orderd',\n",
      "       'Time_Order_picked', 'Weather_conditions', 'Road_traffic_density',\n",
      "       'Vehicle_condition', 'Type_of_order', 'Type_of_vehicle',\n",
      "       'multiple_deliveries', 'Festival', 'City', 'Time_taken(min)'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Update Column Names\n",
    "def update_column_name(df):\n",
    "    #Renaming Weatherconditions column\n",
    "    df.rename(columns={'Weatherconditions':'Weather_conditions'},inplace=True)\n",
    "update_column_name(df_train)\n",
    "print(df_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "3789b101-5ea9-4d4b-8b37-7b9a6e6af8c5",
   "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": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract relevant values from column\n",
    "def extract_column_value(df):\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",
    "    #Extract Weather conditions\n",
    "    df[\"Weather_conditions\"]=df[\"Weather_conditions\"].apply(lambda x: x.split(' ')[1].strip())\n",
    "    #Extract city code from Delivery person ID\n",
    "    df[\"City_code\"]=df[\"Delivery_person_ID\"].str.split(\"RES\", expand=True)[0]\n",
    "extract_column_value(df_train)\n",
    "df_train[[\"Time_taken(min)\", \"Weather_conditions\", \"City_code\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "id": "b6a9e91c-99e4-4277-b3c2-00c2d5b3c040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before No. of columns:  21\n",
      "After No. of colums:  19\n"
     ]
    }
   ],
   "source": [
    "#Drop Columns which won't be use for building model\n",
    "def drop_columns(df):\n",
    "    df.drop(['ID', 'Delivery_person_ID'], axis=1, inplace=True)\n",
    "print(\"Before No. of columns: \", df_train.shape[1])\n",
    "drop_columns(df_train)\n",
    "print(\"After No. of colums: \", df_train.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "04f3ce07-8566-466b-a54b-55f16c327ea1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no duplicate values 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 values present\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "id": "8ef428bd-b3ec-4e7f-833d-1698763bb4cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Update datatypes\n",
    "def update_datatype(df):\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",
    "    df['Order_Date']=pd.to_datetime(df['Order_Date'],format=\"%d-%m-%Y\")\n",
    "\n",
    "update_datatype(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "id": "08cca34d-c596-4deb-bc0c-ca92c400e8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert String 'NaN' to np.nan\n",
    "def convert_nan(df):\n",
    "    df.replace(\"NaN\", float(np.nan), regex=True, inplace=True)\n",
    "\n",
    "convert_nan(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "bf95ff92-bfbe-4588-8a2b-561d7a9743e2",
   "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": 532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check null values\n",
    "df_train.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "01a695fe-8680-4196-ab1c-9a1aad6a36af",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1500 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's explore columns that have null values\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",
    "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": 534,
   "id": "881394a1-8f9f-4a51-ac7d-a2276c7ffeca",
   "metadata": {
    "scrolled": true
   },
   "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                0\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": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handle null values\n",
    "def handle_null_values(df):\n",
    "    \n",
    "    df[\"Delivery_person_Age\"] = df[\"Delivery_person_Age\"].fillna(np.random.choice(df[\"Delivery_person_Age\"]))\n",
    "    df[\"Weather_conditions\"] = df[\"Weather_conditions\"].fillna(np.random.choice(df[\"Weather_conditions\"]))\n",
    "    df[\"City\"] = df[\"City\"].fillna(df[\"City\"].mode()[0])\n",
    "    df[\"Festival\"] = df[\"Festival\"].fillna(df[\"Festival\"].mode()[0])\n",
    "    df[\"multiple_deliveries\"] = df[\"multiple_deliveries\"].fillna(df[\"multiple_deliveries\"].mode()[0])\n",
    "    df[\"Road_traffic_density\"] = df[\"Road_traffic_density\"].fillna(df[\"Road_traffic_density\"].mode()[0])\n",
    "    df[\"Delivery_person_Ratings\"] = df[\"Delivery_person_Ratings\"].fillna(df[\"Delivery_person_Ratings\"].median())\n",
    "\n",
    "# Apply the function to df_train\n",
    "handle_null_values(df_train)\n",
    "\n",
    "# Check for remaining null values\n",
    "df_train.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a4ff76-5eb1-4fba-b7f4-a0fe5be9752a",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "id": "b890bd18-9fda-41ee-bc4d-92218e1204dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "\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>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>Weather_conditions</th>\n",
       "      <th>...</th>\n",
       "      <th>quarter</th>\n",
       "      <th>year</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>is_month_start</th>\n",
       "      <th>is_month_end</th>\n",
       "      <th>is_quarter_start</th>\n",
       "      <th>is_quarter_end</th>\n",
       "      <th>is_year_start</th>\n",
       "      <th>is_year_end</th>\n",
       "      <th>is_weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.0</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>2022-03-19</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>11:45:00</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.0</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>2022-03-25</td>\n",
       "      <td>19:45:00</td>\n",
       "      <td>19:50:00</td>\n",
       "      <td>Stormy</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.0</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>2022-03-19</td>\n",
       "      <td>08:30:00</td>\n",
       "      <td>08:45:00</td>\n",
       "      <td>Sandstorms</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</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>2022-04-05</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>18:10:00</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</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>2022-03-26</td>\n",
       "      <td>13:30:00</td>\n",
       "      <td>13:45:00</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Delivery_person_Age  Delivery_person_Ratings  Restaurant_latitude  \\\n",
       "0                 37.0                      4.9            22.745049   \n",
       "1                 34.0                      4.5            12.913041   \n",
       "2                 23.0                      4.4            12.914264   \n",
       "3                 38.0                      4.7            11.003669   \n",
       "4                 32.0                      4.6            12.972793   \n",
       "\n",
       "   Restaurant_longitude  Delivery_location_latitude  \\\n",
       "0             75.892471                   22.765049   \n",
       "1             77.683237                   13.043041   \n",
       "2             77.678400                   12.924264   \n",
       "3             76.976494                   11.053669   \n",
       "4             80.249982                   13.012793   \n",
       "\n",
       "   Delivery_location_longitude Order_Date Time_Orderd Time_Order_picked  \\\n",
       "0                    75.912471 2022-03-19    11:30:00          11:45:00   \n",
       "1                    77.813237 2022-03-25    19:45:00          19:50:00   \n",
       "2                    77.688400 2022-03-19    08:30:00          08:45:00   \n",
       "3                    77.026494 2022-04-05    18:00:00          18:10:00   \n",
       "4                    80.289982 2022-03-26    13:30:00          13:45:00   \n",
       "\n",
       "  Weather_conditions  ... quarter  year day_of_week is_month_start  \\\n",
       "0              Sunny  ...       1  2022           5              0   \n",
       "1             Stormy  ...       1  2022           4              0   \n",
       "2         Sandstorms  ...       1  2022           5              0   \n",
       "3              Sunny  ...       2  2022           1              0   \n",
       "4             Cloudy  ...       1  2022           5              0   \n",
       "\n",
       "   is_month_end is_quarter_start is_quarter_end  is_year_start is_year_end  \\\n",
       "0             0                0              0              0           0   \n",
       "1             0                0              0              0           0   \n",
       "2             0                0              0              0           0   \n",
       "3             0                0              0              0           0   \n",
       "4             0                0              0              0           0   \n",
       "\n",
       "   is_weekend  \n",
       "0           1  \n",
       "1           0  \n",
       "2           1  \n",
       "3           0  \n",
       "4           1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extract_date_features(data):\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)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "id": "30820d9a-8b1a-408f-b651-5eee268ab2d6",
   "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",
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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>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>Weather_conditions</th>\n",
       "      <th>Road_traffic_density</th>\n",
       "      <th>Vehicle_condition</th>\n",
       "      <th>Type_of_order</th>\n",
       "      <th>...</th>\n",
       "      <th>year</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>is_month_start</th>\n",
       "      <th>is_month_end</th>\n",
       "      <th>is_quarter_start</th>\n",
       "      <th>is_quarter_end</th>\n",
       "      <th>is_year_start</th>\n",
       "      <th>is_year_end</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>order_prepare_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.0</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>Sunny</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>Snack</td>\n",
       "      <td>...</td>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.0</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>Stormy</td>\n",
       "      <td>Jam</td>\n",
       "      <td>2</td>\n",
       "      <td>Snack</td>\n",
       "      <td>...</td>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.0</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>Sandstorms</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>Drinks</td>\n",
       "      <td>...</td>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</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>Sunny</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>...</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</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>Cloudy</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>Snack</td>\n",
       "      <td>...</td>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Delivery_person_Age  Delivery_person_Ratings  Restaurant_latitude  \\\n",
       "0                 37.0                      4.9            22.745049   \n",
       "1                 34.0                      4.5            12.913041   \n",
       "2                 23.0                      4.4            12.914264   \n",
       "3                 38.0                      4.7            11.003669   \n",
       "4                 32.0                      4.6            12.972793   \n",
       "\n",
       "   Restaurant_longitude  Delivery_location_latitude  \\\n",
       "0             75.892471                   22.765049   \n",
       "1             77.683237                   13.043041   \n",
       "2             77.678400                   12.924264   \n",
       "3             76.976494                   11.053669   \n",
       "4             80.249982                   13.012793   \n",
       "\n",
       "   Delivery_location_longitude Weather_conditions Road_traffic_density  \\\n",
       "0                    75.912471              Sunny                High    \n",
       "1                    77.813237             Stormy                 Jam    \n",
       "2                    77.688400         Sandstorms                 Low    \n",
       "3                    77.026494              Sunny              Medium    \n",
       "4                    80.289982             Cloudy                High    \n",
       "\n",
       "   Vehicle_condition Type_of_order  ...  year  day_of_week is_month_start  \\\n",
       "0                  2        Snack   ...  2022            5              0   \n",
       "1                  2        Snack   ...  2022            4              0   \n",
       "2                  0       Drinks   ...  2022            5              0   \n",
       "3                  0       Buffet   ...  2022            1              0   \n",
       "4                  1        Snack   ...  2022            5              0   \n",
       "\n",
       "  is_month_end  is_quarter_start is_quarter_end  is_year_start  is_year_end  \\\n",
       "0            0                 0              0              0            0   \n",
       "1            0                 0              0              0            0   \n",
       "2            0                 0              0              0            0   \n",
       "3            0                 0              0              0            0   \n",
       "4            0                 0              0              0            0   \n",
       "\n",
       "   is_weekend  order_prepare_time  \n",
       "0           1                15.0  \n",
       "1           0                 5.0  \n",
       "2           1                15.0  \n",
       "3           0                10.0  \n",
       "4           1                15.0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calculate_time_diff(df):\n",
    "    # Convert the time columns to timedelta\n",
    "    df['Time_Orderd'] = pd.to_timedelta(df['Time_Orderd'])\n",
    "    df['Time_Order_picked'] = pd.to_timedelta(df['Time_Order_picked'])\n",
    "\n",
    "    # Correctly format the picked time, handling cases where picked time is on the next day\n",
    "    df['Time_Order_picked_formatted'] = df['Order_Date'] + df['Time_Order_picked']\n",
    "    mask = df['Time_Order_picked'] < df['Time_Orderd']\n",
    "    df.loc[mask, 'Time_Order_picked_formatted'] += pd.DateOffset(days=1)\n",
    "\n",
    "    # Format the ordered time\n",
    "    df['Time_Ordered_formatted'] = df['Order_Date'] + df['Time_Orderd']\n",
    "\n",
    "    # Calculate the order preparation time in minutes\n",
    "    df['order_prepare_time'] = (df['Time_Order_picked_formatted'] - df['Time_Ordered_formatted']).dt.total_seconds() / 60\n",
    "\n",
    "    # Handle null values by assigning the result of fillna() to the column explicitly\n",
    "    df['order_prepare_time'] = df['order_prepare_time'].fillna(df['order_prepare_time'].median())\n",
    "\n",
    "    # Drop all the time & date related columns\n",
    "    df.drop(['Time_Orderd', 'Time_Order_picked', 'Time_Ordered_formatted', 'Time_Order_picked_formatted', 'Order_Date'], axis=1, inplace=True)\n",
    "\n",
    "# Assuming df_train is your DataFrame\n",
    "calculate_time_diff(df_train)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "id": "3778649d-d3b5-41e6-b184-cf4455acb06b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>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>Weather_conditions</th>\n",
       "      <th>Road_traffic_density</th>\n",
       "      <th>Vehicle_condition</th>\n",
       "      <th>Type_of_order</th>\n",
       "      <th>...</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>is_month_start</th>\n",
       "      <th>is_month_end</th>\n",
       "      <th>is_quarter_start</th>\n",
       "      <th>is_quarter_end</th>\n",
       "      <th>is_year_start</th>\n",
       "      <th>is_year_end</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>order_prepare_time</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.0</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>Sunny</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>Snack</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.0</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>Stormy</td>\n",
       "      <td>Jam</td>\n",
       "      <td>2</td>\n",
       "      <td>Snack</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.0</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>Sandstorms</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>Drinks</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</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>Sunny</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</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>Cloudy</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>Snack</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Delivery_person_Age  Delivery_person_Ratings  Restaurant_latitude  \\\n",
       "0                 37.0                      4.9            22.745049   \n",
       "1                 34.0                      4.5            12.913041   \n",
       "2                 23.0                      4.4            12.914264   \n",
       "3                 38.0                      4.7            11.003669   \n",
       "4                 32.0                      4.6            12.972793   \n",
       "\n",
       "   Restaurant_longitude  Delivery_location_latitude  \\\n",
       "0             75.892471                   22.765049   \n",
       "1             77.683237                   13.043041   \n",
       "2             77.678400                   12.924264   \n",
       "3             76.976494                   11.053669   \n",
       "4             80.249982                   13.012793   \n",
       "\n",
       "   Delivery_location_longitude Weather_conditions Road_traffic_density  \\\n",
       "0                    75.912471              Sunny                High    \n",
       "1                    77.813237             Stormy                 Jam    \n",
       "2                    77.688400         Sandstorms                 Low    \n",
       "3                    77.026494              Sunny              Medium    \n",
       "4                    80.289982             Cloudy                High    \n",
       "\n",
       "   Vehicle_condition Type_of_order  ... day_of_week  is_month_start  \\\n",
       "0                  2        Snack   ...           5               0   \n",
       "1                  2        Snack   ...           4               0   \n",
       "2                  0       Drinks   ...           5               0   \n",
       "3                  0       Buffet   ...           1               0   \n",
       "4                  1        Snack   ...           5               0   \n",
       "\n",
       "  is_month_end is_quarter_start  is_quarter_end is_year_start  is_year_end  \\\n",
       "0            0                0               0             0            0   \n",
       "1            0                0               0             0            0   \n",
       "2            0                0               0             0            0   \n",
       "3            0                0               0             0            0   \n",
       "4            0                0               0             0            0   \n",
       "\n",
       "   is_weekend  order_prepare_time  distance  \n",
       "0           1                15.0         3  \n",
       "1           0                 5.0        20  \n",
       "2           1                15.0         1  \n",
       "3           0                10.0         7  \n",
       "4           1                15.0         6  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate distance between restaurant location & delivery location\n",
    "from geopy.distance import geodesic\n",
    "\n",
    "def calculate_distance(df):\n",
    "    # Initialize the distance column with zeros\n",
    "    df[\"distance\"] = np.zeros(len(df))\n",
    "\n",
    "    # Get the restaurant and delivery coordinates as numpy arrays\n",
    "    restaurant_coordinates = df[['Restaurant_latitude', 'Restaurant_longitude']].to_numpy()\n",
    "    delivery_location_coordinates = df[['Delivery_location_latitude', 'Delivery_location_longitude']].to_numpy()\n",
    "\n",
    "    # Calculate geodesic distance between restaurant and delivery locations\n",
    "    df[\"distance\"] = np.array([geodesic(restaurant, delivery).kilometers for restaurant, delivery in zip(restaurant_coordinates, delivery_location_coordinates)])\n",
    "\n",
    "    # Extract numeric part from the distance (if needed)\n",
    "    df[\"distance\"] = df[\"distance\"].astype(\"str\").str.extract(r'(\\d+)').astype(\"int64\")\n",
    "\n",
    "# Apply the function to df_train\n",
    "calculate_distance(df_train)\n",
    "df_train.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34d4e990-742f-4277-b410-62ea95a8695c",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a07c6f9-275b-4aa8-b5eb-d9ba25bed15f",
   "metadata": {},
   "source": [
    "### Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "id": "cba7fc51-7479-461e-b7e0-cf260302e2a2",
   "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>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>Weather_conditions</th>\n",
       "      <th>Road_traffic_density</th>\n",
       "      <th>Vehicle_condition</th>\n",
       "      <th>Type_of_order</th>\n",
       "      <th>...</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>is_month_start</th>\n",
       "      <th>is_month_end</th>\n",
       "      <th>is_quarter_start</th>\n",
       "      <th>is_quarter_end</th>\n",
       "      <th>is_year_start</th>\n",
       "      <th>is_year_end</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>order_prepare_time</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.0</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>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.0</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>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.0</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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</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>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Delivery_person_Age  Delivery_person_Ratings  Restaurant_latitude  \\\n",
       "0                 37.0                      4.9            22.745049   \n",
       "1                 34.0                      4.5            12.913041   \n",
       "2                 23.0                      4.4            12.914264   \n",
       "3                 38.0                      4.7            11.003669   \n",
       "4                 32.0                      4.6            12.972793   \n",
       "\n",
       "   Restaurant_longitude  Delivery_location_latitude  \\\n",
       "0             75.892471                   22.765049   \n",
       "1             77.683237                   13.043041   \n",
       "2             77.678400                   12.924264   \n",
       "3             76.976494                   11.053669   \n",
       "4             80.249982                   13.012793   \n",
       "\n",
       "   Delivery_location_longitude  Weather_conditions  Road_traffic_density  \\\n",
       "0                    75.912471                   4                     0   \n",
       "1                    77.813237                   3                     1   \n",
       "2                    77.688400                   2                     2   \n",
       "3                    77.026494                   4                     3   \n",
       "4                    80.289982                   0                     0   \n",
       "\n",
       "   Vehicle_condition  Type_of_order  ...  day_of_week  is_month_start  \\\n",
       "0                  2              3  ...            5               0   \n",
       "1                  2              3  ...            4               0   \n",
       "2                  0              1  ...            5               0   \n",
       "3                  0              0  ...            1               0   \n",
       "4                  1              3  ...            5               0   \n",
       "\n",
       "   is_month_end  is_quarter_start  is_quarter_end  is_year_start  is_year_end  \\\n",
       "0             0                 0               0              0            0   \n",
       "1             0                 0               0              0            0   \n",
       "2             0                 0               0              0            0   \n",
       "3             0                 0               0              0            0   \n",
       "4             0                 0               0              0            0   \n",
       "\n",
       "   is_weekend  order_prepare_time  distance  \n",
       "0           1                15.0         3  \n",
       "1           0                 5.0        20  \n",
       "2           1                15.0         1  \n",
       "3           0                10.0         7  \n",
       "4           1                15.0         6  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import LabelEncoder from sklearn\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\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)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "id": "8b39692f-fbe3-4959-9d7d-0c7270e42e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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', 'distance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "id": "97c0ee07-681e-4046-a84f-bd71679bb2c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Delivery_person_Age', 'Delivery_person_Ratings', 'Restaurant_latitude', 'Restaurant_longitude', 'Delivery_location_latitude', 'Delivery_location_longitude', 'Weather_conditions', 'Road_traffic_density', 'Vehicle_condition', 'Type_of_order', 'Type_of_vehicle', 'multiple_deliveries', 'Festival', 'City', 'Time_taken(min)', 'City_code', 'day', 'month', 'quarter', 'year', 'day_of_week', 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', 'is_year_start', 'is_year_end', 'is_weekend', 'order_prepare_time', 'distance']\n"
     ]
    }
   ],
   "source": [
    "# Print all column names to check for any hidden characters or formatting issues\n",
    "print(df_train.columns.tolist())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed418d2-f540-48e5-a3db-b2f049685f1e",
   "metadata": {},
   "source": [
    "## Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "id": "c102fba3-628c-4ab8-a92a-feca24376a6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36474, 29)\n",
      "(36474,)\n",
      "(9119, 29)\n",
      "(9119,)\n"
     ]
    }
   ],
   "source": [
    "# Check the column names and strip any leading/trailing whitespace\n",
    "df_train.columns = df_train.columns.str.strip()\n",
    "\n",
    "# Now try dropping the column again\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",
    "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=42)\n",
    "\n",
    "# Print shapes\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8a1e06-0f05-47cc-a03a-ce86b786b6a1",
   "metadata": {},
   "source": [
    "## Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "id": "82c635cb-819c-4020-9b61-fcb62302c2cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Create a StandardScaler object\n",
    "scaler=StandardScaler()\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": "markdown",
   "id": "5b9da068-98b5-42e3-a3bb-2351df441675",
   "metadata": {},
   "source": [
    "## Model Building\n",
    "### Steps\n",
    "1. Employ cross-validation & hyper parameter tuning to determine the optimal regression model.\n",
    "2. Construct the food delivery prediction model using the identified best model.\n",
    "3. Evaluate the model's performance on the testing data to assess its accuracy and reliability."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98caab00-aae0-4da8-9d62-57532bd6f40b",
   "metadata": {},
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "id": "6718096f-3baa-44b1-9671-8e5470c5c6a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression\n",
      "Best parameters:  {}\n",
      "Best R2 score:  0.42195334486780717\n",
      "\n",
      "DecisionTreeRegressor\n",
      "Best parameters:  {'max_depth': 7}\n",
      "Best R2 score:  0.7170948555927096\n",
      "\n",
      "RandomForestRegressor\n",
      "Best parameters:  {'n_estimators': 300}\n",
      "Best R2 score:  0.811648817798079\n",
      "\n",
      "XGBRegressor\n",
      "Best parameters:  {'max_depth': 7, 'n_estimators': 20}\n",
      "Best R2 score:  0.8212592396880698\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Find the best model\n",
    "models=[\n",
    "    LinearRegression(),\n",
    "    DecisionTreeRegressor(),\n",
    "    RandomForestRegressor(),\n",
    "    xgb.XGBRegressor(),\n",
    "]\n",
    "\n",
    "param_grid=[\n",
    "    {},\n",
    "    {\"max_depth\":[3,5,7]},\n",
    "    {\"n_estimators\":[100,200,300]},\n",
    "    {\"n_estimators\":[20, 25, 30], \"max_depth\":[5,7,9]}\n",
    "]\n",
    "\n",
    "for i, model in enumerate(models):\n",
    "    grid_search=GridSearchCV(model, param_grid[i], cv=5, scoring=\"r2\")\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    print(f\"{model.__class__.__name__}\")\n",
    "    print(\"Best parameters: \", grid_search.best_params_)\n",
    "    print(\"Best R2 score: \",grid_search.best_score_)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3aa5ca9-820a-4482-9662-69ff90ea2f4f",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "id": "ebe37034-29fa-4822-ba0f-24c016ded17d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><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": 552,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a XGB regressor model\n",
    "model=xgb.XGBRegressor(n_estimators=20, max_depth=9)\n",
    "\n",
    "\n",
    "# Fit the model on the training data\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b58fd6-f9f2-4c39-9a1b-fb4c1a7993d7",
   "metadata": {},
   "source": [
    "##### In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. \n",
    "##### On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecd0b017-3285-4139-8f5e-10bbdfaa0f3f",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "id": "516f288e-0b4d-490f-8e48-22d81a7602ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 3.15\n",
      "Mean Squared Error (MSE): 15.65\n",
      "Root Mean Squared Error (RMSE): 3.96\n",
      "R-squared (R2) Score: 0.82\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\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": "markdown",
   "id": "2fa16666-0cea-4bfe-815c-941116c7d06d",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "In conclusion, the food delivery prediction model was developed using XGBoost, achieving an impressive R2 score of 0.82. Moving forward, potential enhancements include identifying the best features, conducting additional feature engineering, and exploring other optimization techniques to further improve the model's performance and accuracy. These steps will contribute to fine-tuning the model and unlocking its full potential in predicting food delivery timings accurately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "id": "e1b98e10-ec00-44fa-be47-0bcfbb95f1f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved to model.pkl\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "# Define the file name\n",
    "filename=\"model.pkl\"\n",
    "\n",
    "# Save the model using pickle\n",
    "with open(filename,\"wb\") as file:\n",
    "    pickle.dump(model,file)\n",
    "print(f\"Model saved to {filename}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "id": "7b6961f4-1e30-4193-935c-be678b3d005e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved to scaler.pkl\n"
     ]
    }
   ],
   "source": [
    "# Define the filename\n",
    "\n",
    "filename_scaler=\"scaler.pkl\"\n",
    "\n",
    "# Save the model using pickle\n",
    "with open(filename_scaler, \"wb\") as file:\n",
    "    pickle.dump(scaler, file)\n",
    "print(f\"Model saved to {filename_scaler}\")"
   ]
  }
 ],
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