doordash.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation for Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>market_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>actual_delivery_time</th>\n",
       "      <th>store_id</th>\n",
       "      <th>store_primary_category</th>\n",
       "      <th>order_protocol</th>\n",
       "      <th>total_items</th>\n",
       "      <th>subtotal</th>\n",
       "      <th>num_distinct_items</th>\n",
       "      <th>min_item_price</th>\n",
       "      <th>max_item_price</th>\n",
       "      <th>total_onshift_dashers</th>\n",
       "      <th>total_busy_dashers</th>\n",
       "      <th>total_outstanding_orders</th>\n",
       "      <th>estimated_order_place_duration</th>\n",
       "      <th>estimated_store_to_consumer_driving_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2015-02-06 22:24:17</td>\n",
       "      <td>2015-02-06 23:27:16</td>\n",
       "      <td>1845</td>\n",
       "      <td>american</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3441</td>\n",
       "      <td>4</td>\n",
       "      <td>557</td>\n",
       "      <td>1239</td>\n",
       "      <td>33.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>446</td>\n",
       "      <td>861.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2015-02-10 21:49:25</td>\n",
       "      <td>2015-02-10 22:56:29</td>\n",
       "      <td>5477</td>\n",
       "      <td>mexican</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1</td>\n",
       "      <td>1400</td>\n",
       "      <td>1400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446</td>\n",
       "      <td>690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-01-22 20:39:28</td>\n",
       "      <td>2015-01-22 21:09:09</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446</td>\n",
       "      <td>690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-02-03 21:21:45</td>\n",
       "      <td>2015-02-03 22:13:00</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6900</td>\n",
       "      <td>5</td>\n",
       "      <td>600</td>\n",
       "      <td>1800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446</td>\n",
       "      <td>289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-02-15 02:40:36</td>\n",
       "      <td>2015-02-15 03:20:26</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3900</td>\n",
       "      <td>3</td>\n",
       "      <td>1100</td>\n",
       "      <td>1600</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>446</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   market_id           created_at actual_delivery_time  store_id  \\\n",
       "0        1.0  2015-02-06 22:24:17  2015-02-06 23:27:16      1845   \n",
       "1        2.0  2015-02-10 21:49:25  2015-02-10 22:56:29      5477   \n",
       "2        3.0  2015-01-22 20:39:28  2015-01-22 21:09:09      5477   \n",
       "3        3.0  2015-02-03 21:21:45  2015-02-03 22:13:00      5477   \n",
       "4        3.0  2015-02-15 02:40:36  2015-02-15 03:20:26      5477   \n",
       "\n",
       "  store_primary_category  order_protocol  total_items  subtotal  \\\n",
       "0               american             1.0            4      3441   \n",
       "1                mexican             2.0            1      1900   \n",
       "2                    NaN             1.0            1      1900   \n",
       "3                    NaN             1.0            6      6900   \n",
       "4                    NaN             1.0            3      3900   \n",
       "\n",
       "   num_distinct_items  min_item_price  max_item_price  total_onshift_dashers  \\\n",
       "0                   4             557            1239                   33.0   \n",
       "1                   1            1400            1400                    1.0   \n",
       "2                   1            1900            1900                    1.0   \n",
       "3                   5             600            1800                    1.0   \n",
       "4                   3            1100            1600                    6.0   \n",
       "\n",
       "   total_busy_dashers  total_outstanding_orders  \\\n",
       "0                14.0                      21.0   \n",
       "1                 2.0                       2.0   \n",
       "2                 0.0                       0.0   \n",
       "3                 1.0                       2.0   \n",
       "4                 6.0                       9.0   \n",
       "\n",
       "   estimated_order_place_duration  \\\n",
       "0                             446   \n",
       "1                             446   \n",
       "2                             446   \n",
       "3                             446   \n",
       "4                             446   \n",
       "\n",
       "   estimated_store_to_consumer_driving_duration  \n",
       "0                                         861.0  \n",
       "1                                         690.0  \n",
       "2                                         690.0  \n",
       "3                                         289.0  \n",
       "4                                         650.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data = pd.read_csv('doordash_historical_data.csv')\n",
    "historical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 197428 entries, 0 to 197427\n",
      "Data columns (total 16 columns):\n",
      " #   Column                                        Non-Null Count   Dtype  \n",
      "---  ------                                        --------------   -----  \n",
      " 0   market_id                                     196441 non-null  float64\n",
      " 1   created_at                                    197428 non-null  object \n",
      " 2   actual_delivery_time                          197421 non-null  object \n",
      " 3   store_id                                      197428 non-null  int64  \n",
      " 4   store_primary_category                        192668 non-null  object \n",
      " 5   order_protocol                                196433 non-null  float64\n",
      " 6   total_items                                   197428 non-null  int64  \n",
      " 7   subtotal                                      197428 non-null  int64  \n",
      " 8   num_distinct_items                            197428 non-null  int64  \n",
      " 9   min_item_price                                197428 non-null  int64  \n",
      " 10  max_item_price                                197428 non-null  int64  \n",
      " 11  total_onshift_dashers                         181166 non-null  float64\n",
      " 12  total_busy_dashers                            181166 non-null  float64\n",
      " 13  total_outstanding_orders                      181166 non-null  float64\n",
      " 14  estimated_order_place_duration                197428 non-null  int64  \n",
      " 15  estimated_store_to_consumer_driving_duration  196902 non-null  float64\n",
      "dtypes: float64(6), int64(7), object(3)\n",
      "memory usage: 24.1+ MB\n"
     ]
    }
   ],
   "source": [
    "historical_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `created_at` and `actual_delivery_time` are not in timestamps format, they're objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 197428 entries, 0 to 197427\n",
      "Data columns (total 16 columns):\n",
      " #   Column                                        Non-Null Count   Dtype         \n",
      "---  ------                                        --------------   -----         \n",
      " 0   market_id                                     196441 non-null  float64       \n",
      " 1   created_at                                    197428 non-null  datetime64[ns]\n",
      " 2   actual_delivery_time                          197421 non-null  datetime64[ns]\n",
      " 3   store_id                                      197428 non-null  int64         \n",
      " 4   store_primary_category                        192668 non-null  object        \n",
      " 5   order_protocol                                196433 non-null  float64       \n",
      " 6   total_items                                   197428 non-null  int64         \n",
      " 7   subtotal                                      197428 non-null  int64         \n",
      " 8   num_distinct_items                            197428 non-null  int64         \n",
      " 9   min_item_price                                197428 non-null  int64         \n",
      " 10  max_item_price                                197428 non-null  int64         \n",
      " 11  total_onshift_dashers                         181166 non-null  float64       \n",
      " 12  total_busy_dashers                            181166 non-null  float64       \n",
      " 13  total_outstanding_orders                      181166 non-null  float64       \n",
      " 14  estimated_order_place_duration                197428 non-null  int64         \n",
      " 15  estimated_store_to_consumer_driving_duration  196902 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(6), int64(7), object(1)\n",
      "memory usage: 24.1+ MB\n"
     ]
    }
   ],
   "source": [
    "historical_data['created_at'] = pd.to_datetime(historical_data['created_at'])\n",
    "historical_data['actual_delivery_time'] = pd.to_datetime(historical_data['actual_delivery_time'])\n",
    "historical_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>market_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>actual_delivery_time</th>\n",
       "      <th>store_id</th>\n",
       "      <th>store_primary_category</th>\n",
       "      <th>order_protocol</th>\n",
       "      <th>total_items</th>\n",
       "      <th>subtotal</th>\n",
       "      <th>num_distinct_items</th>\n",
       "      <th>min_item_price</th>\n",
       "      <th>max_item_price</th>\n",
       "      <th>total_onshift_dashers</th>\n",
       "      <th>total_busy_dashers</th>\n",
       "      <th>total_outstanding_orders</th>\n",
       "      <th>estimated_order_place_duration</th>\n",
       "      <th>estimated_store_to_consumer_driving_duration</th>\n",
       "      <th>actual_total_delivery_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2015-02-06 22:24:17</td>\n",
       "      <td>2015-02-06 23:27:16</td>\n",
       "      <td>1845</td>\n",
       "      <td>american</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3441</td>\n",
       "      <td>4</td>\n",
       "      <td>557</td>\n",
       "      <td>1239</td>\n",
       "      <td>33.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>446</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2015-02-10 21:49:25</td>\n",
       "      <td>2015-02-10 22:56:29</td>\n",
       "      <td>5477</td>\n",
       "      <td>mexican</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1</td>\n",
       "      <td>1400</td>\n",
       "      <td>1400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446</td>\n",
       "      <td>690.0</td>\n",
       "      <td>4024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-01-22 20:39:28</td>\n",
       "      <td>2015-01-22 21:09:09</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446</td>\n",
       "      <td>690.0</td>\n",
       "      <td>1781.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-02-03 21:21:45</td>\n",
       "      <td>2015-02-03 22:13:00</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6900</td>\n",
       "      <td>5</td>\n",
       "      <td>600</td>\n",
       "      <td>1800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446</td>\n",
       "      <td>289.0</td>\n",
       "      <td>3075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-02-15 02:40:36</td>\n",
       "      <td>2015-02-15 03:20:26</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3900</td>\n",
       "      <td>3</td>\n",
       "      <td>1100</td>\n",
       "      <td>1600</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>446</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2390.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   market_id          created_at actual_delivery_time  store_id  \\\n",
       "0        1.0 2015-02-06 22:24:17  2015-02-06 23:27:16      1845   \n",
       "1        2.0 2015-02-10 21:49:25  2015-02-10 22:56:29      5477   \n",
       "2        3.0 2015-01-22 20:39:28  2015-01-22 21:09:09      5477   \n",
       "3        3.0 2015-02-03 21:21:45  2015-02-03 22:13:00      5477   \n",
       "4        3.0 2015-02-15 02:40:36  2015-02-15 03:20:26      5477   \n",
       "\n",
       "  store_primary_category  order_protocol  total_items  subtotal  \\\n",
       "0               american             1.0            4      3441   \n",
       "1                mexican             2.0            1      1900   \n",
       "2                    NaN             1.0            1      1900   \n",
       "3                    NaN             1.0            6      6900   \n",
       "4                    NaN             1.0            3      3900   \n",
       "\n",
       "   num_distinct_items  min_item_price  max_item_price  total_onshift_dashers  \\\n",
       "0                   4             557            1239                   33.0   \n",
       "1                   1            1400            1400                    1.0   \n",
       "2                   1            1900            1900                    1.0   \n",
       "3                   5             600            1800                    1.0   \n",
       "4                   3            1100            1600                    6.0   \n",
       "\n",
       "   total_busy_dashers  total_outstanding_orders  \\\n",
       "0                14.0                      21.0   \n",
       "1                 2.0                       2.0   \n",
       "2                 0.0                       0.0   \n",
       "3                 1.0                       2.0   \n",
       "4                 6.0                       9.0   \n",
       "\n",
       "   estimated_order_place_duration  \\\n",
       "0                             446   \n",
       "1                             446   \n",
       "2                             446   \n",
       "3                             446   \n",
       "4                             446   \n",
       "\n",
       "   estimated_store_to_consumer_driving_duration  \\\n",
       "0                                         861.0   \n",
       "1                                         690.0   \n",
       "2                                         690.0   \n",
       "3                                         289.0   \n",
       "4                                         650.0   \n",
       "\n",
       "   actual_total_delivery_duration  \n",
       "0                          3779.0  \n",
       "1                          4024.0  \n",
       "2                          1781.0  \n",
       "3                          3075.0  \n",
       "4                          2390.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "historical_data['actual_total_delivery_duration'] = (historical_data['actual_delivery_time'] - historical_data['created_at']).dt.total_seconds()\n",
    "historical_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Busy Dashers Ratio\n",
    "- The number of total dashers and number of busy dashers at any given time of order placement is useful.\n",
    "- But both are subject to changes based on area and time\n",
    "- So, a better variable would be the ratio of busy dashers to total available dashers at any time and place.\n",
    "- Higher the ratio, longer the wait times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>market_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>actual_delivery_time</th>\n",
       "      <th>store_id</th>\n",
       "      <th>store_primary_category</th>\n",
       "      <th>order_protocol</th>\n",
       "      <th>total_items</th>\n",
       "      <th>subtotal</th>\n",
       "      <th>num_distinct_items</th>\n",
       "      <th>min_item_price</th>\n",
       "      <th>max_item_price</th>\n",
       "      <th>total_onshift_dashers</th>\n",
       "      <th>total_busy_dashers</th>\n",
       "      <th>total_outstanding_orders</th>\n",
       "      <th>estimated_order_place_duration</th>\n",
       "      <th>estimated_store_to_consumer_driving_duration</th>\n",
       "      <th>actual_total_delivery_duration</th>\n",
       "      <th>busy_dashers_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2015-02-06 22:24:17</td>\n",
       "      <td>2015-02-06 23:27:16</td>\n",
       "      <td>1845</td>\n",
       "      <td>american</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3441</td>\n",
       "      <td>4</td>\n",
       "      <td>557</td>\n",
       "      <td>1239</td>\n",
       "      <td>33.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>446</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3779.0</td>\n",
       "      <td>0.424242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2015-02-10 21:49:25</td>\n",
       "      <td>2015-02-10 22:56:29</td>\n",
       "      <td>5477</td>\n",
       "      <td>mexican</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1</td>\n",
       "      <td>1400</td>\n",
       "      <td>1400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446</td>\n",
       "      <td>690.0</td>\n",
       "      <td>4024.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-01-22 20:39:28</td>\n",
       "      <td>2015-01-22 21:09:09</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446</td>\n",
       "      <td>690.0</td>\n",
       "      <td>1781.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-02-03 21:21:45</td>\n",
       "      <td>2015-02-03 22:13:00</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6900</td>\n",
       "      <td>5</td>\n",
       "      <td>600</td>\n",
       "      <td>1800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446</td>\n",
       "      <td>289.0</td>\n",
       "      <td>3075.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-02-15 02:40:36</td>\n",
       "      <td>2015-02-15 03:20:26</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3900</td>\n",
       "      <td>3</td>\n",
       "      <td>1100</td>\n",
       "      <td>1600</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>446</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2390.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   market_id          created_at actual_delivery_time  store_id  \\\n",
       "0        1.0 2015-02-06 22:24:17  2015-02-06 23:27:16      1845   \n",
       "1        2.0 2015-02-10 21:49:25  2015-02-10 22:56:29      5477   \n",
       "2        3.0 2015-01-22 20:39:28  2015-01-22 21:09:09      5477   \n",
       "3        3.0 2015-02-03 21:21:45  2015-02-03 22:13:00      5477   \n",
       "4        3.0 2015-02-15 02:40:36  2015-02-15 03:20:26      5477   \n",
       "\n",
       "  store_primary_category  order_protocol  total_items  subtotal  \\\n",
       "0               american             1.0            4      3441   \n",
       "1                mexican             2.0            1      1900   \n",
       "2                    NaN             1.0            1      1900   \n",
       "3                    NaN             1.0            6      6900   \n",
       "4                    NaN             1.0            3      3900   \n",
       "\n",
       "   num_distinct_items  min_item_price  max_item_price  total_onshift_dashers  \\\n",
       "0                   4             557            1239                   33.0   \n",
       "1                   1            1400            1400                    1.0   \n",
       "2                   1            1900            1900                    1.0   \n",
       "3                   5             600            1800                    1.0   \n",
       "4                   3            1100            1600                    6.0   \n",
       "\n",
       "   total_busy_dashers  total_outstanding_orders  \\\n",
       "0                14.0                      21.0   \n",
       "1                 2.0                       2.0   \n",
       "2                 0.0                       0.0   \n",
       "3                 1.0                       2.0   \n",
       "4                 6.0                       9.0   \n",
       "\n",
       "   estimated_order_place_duration  \\\n",
       "0                             446   \n",
       "1                             446   \n",
       "2                             446   \n",
       "3                             446   \n",
       "4                             446   \n",
       "\n",
       "   estimated_store_to_consumer_driving_duration  \\\n",
       "0                                         861.0   \n",
       "1                                         690.0   \n",
       "2                                         690.0   \n",
       "3                                         289.0   \n",
       "4                                         650.0   \n",
       "\n",
       "   actual_total_delivery_duration  busy_dashers_ratio  \n",
       "0                          3779.0            0.424242  \n",
       "1                          4024.0            2.000000  \n",
       "2                          1781.0            0.000000  \n",
       "3                          3075.0            1.000000  \n",
       "4                          2390.0            1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data['busy_dashers_ratio'] = historical_data['total_busy_dashers'] / historical_data['total_onshift_dashers']\n",
    "historical_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Non-preparation Duration\n",
    "- Idea is to separate preparation duration (under restaurant's control) and non-preparation duration (not under restaurant's control)\n",
    "- We have `estimated_order_place_duration` and `estimated_store_to_consumer_driving_duration`; we need to sum them to get the non-preparation duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_data['estimated_non_prep_duration'] = historical_data['estimated_order_place_duration'] + historical_data['estimated_store_to_consumer_driving_duration']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation for modeling\n",
    "- Let's start with nominal variables: `market_id`, `store_id` and `order_protocol`\n",
    "- We need to one-hot encode them because they're nominal (not ordinal)\n",
    "- But before we do that, we need to check the number of unique values of all of them (if there are too many, the feature set will explode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique market ids 6\n",
      "Number of unique store ids 6743\n",
      "Number of unique order protocols 7\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of unique market ids\", historical_data['market_id'].nunique())\n",
    "print(\"Number of unique store ids\", historical_data['store_id'].nunique())\n",
    "print(\"Number of unique order protocols\", historical_data['order_protocol'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can one-hot encode `market_id` and `order_protocol` but not `store_id`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>order_protocol_1.0</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_protocol_1.0  order_protocol_2.0  order_protocol_3.0  \\\n",
       "0                   1                   0                   0   \n",
       "1                   0                   1                   0   \n",
       "2                   1                   0                   0   \n",
       "3                   1                   0                   0   \n",
       "4                   1                   0                   0   \n",
       "\n",
       "   order_protocol_4.0  order_protocol_5.0  order_protocol_6.0  \\\n",
       "0                   0                   0                   0   \n",
       "1                   0                   0                   0   \n",
       "2                   0                   0                   0   \n",
       "3                   0                   0                   0   \n",
       "4                   0                   0                   0   \n",
       "\n",
       "   order_protocol_7.0  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dummies for the order protocol\n",
    "order_protocol_dummies = pd.get_dummies(historical_data['order_protocol'])\n",
    "order_protocol_dummies = order_protocol_dummies.add_prefix('order_protocol_')\n",
    "order_protocol_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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      ],
      "text/plain": [
       "   market_id_1.0  market_id_2.0  market_id_3.0  market_id_4.0  market_id_5.0  \\\n",
       "0              1              0              0              0              0   \n",
       "1              0              1              0              0              0   \n",
       "2              0              0              1              0              0   \n",
       "3              0              0              1              0              0   \n",
       "4              0              0              1              0              0   \n",
       "\n",
       "   market_id_6.0  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dummies for the market id\n",
    "market_id_dummies = pd.get_dummies(historical_data['market_id'])\n",
    "market_id_dummies = market_id_dummies.add_prefix('market_id_')\n",
    "market_id_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Forget about `store_id` and instead focus on `store_primary_category`\n",
    "- From the `.info()` cell earlier, we can see that there are missing values in the column\n",
    "- We need to do missing value imputation\n",
    "- We can use the most common category (cuisine category that is) that a restaurant has among all their orders\n",
    "- We will use a reference dictionary for that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dictionary with most repeated categories of each store to fill NULL rows where it is possible\n",
    "store_id_unique = historical_data['store_id'].unique().tolist()\n",
    "store_id_and_category = {\n",
    "    store_id: historical_data[historical_data['store_id'] == store_id]['store_primary_category'].mode()\n",
    "    for store_id in store_id_unique\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "      <th>estimated_order_place_duration</th>\n",
       "      <th>estimated_store_to_consumer_driving_duration</th>\n",
       "      <th>actual_total_delivery_duration</th>\n",
       "      <th>busy_dashers_ratio</th>\n",
       "      <th>estimated_non_prep_duration</th>\n",
       "      <th>nan_free_store_primary_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2015-02-06 22:24:17</td>\n",
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       "      <td>1845</td>\n",
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       "      <td>14.0</td>\n",
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       "      <td>446</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3779.0</td>\n",
       "      <td>0.424242</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>american</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2015-02-10 21:49:25</td>\n",
       "      <td>2015-02-10 22:56:29</td>\n",
       "      <td>5477</td>\n",
       "      <td>mexican</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1</td>\n",
       "      <td>1400</td>\n",
       "      <td>1400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446</td>\n",
       "      <td>690.0</td>\n",
       "      <td>4024.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>indian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-01-22 20:39:28</td>\n",
       "      <td>2015-01-22 21:09:09</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>1900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446</td>\n",
       "      <td>690.0</td>\n",
       "      <td>1781.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>indian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-02-03 21:21:45</td>\n",
       "      <td>2015-02-03 22:13:00</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6900</td>\n",
       "      <td>5</td>\n",
       "      <td>600</td>\n",
       "      <td>1800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446</td>\n",
       "      <td>289.0</td>\n",
       "      <td>3075.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>735.0</td>\n",
       "      <td>indian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-02-15 02:40:36</td>\n",
       "      <td>2015-02-15 03:20:26</td>\n",
       "      <td>5477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3900</td>\n",
       "      <td>3</td>\n",
       "      <td>1100</td>\n",
       "      <td>1600</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>446</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2390.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>indian</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   market_id          created_at actual_delivery_time  store_id  \\\n",
       "0        1.0 2015-02-06 22:24:17  2015-02-06 23:27:16      1845   \n",
       "1        2.0 2015-02-10 21:49:25  2015-02-10 22:56:29      5477   \n",
       "2        3.0 2015-01-22 20:39:28  2015-01-22 21:09:09      5477   \n",
       "3        3.0 2015-02-03 21:21:45  2015-02-03 22:13:00      5477   \n",
       "4        3.0 2015-02-15 02:40:36  2015-02-15 03:20:26      5477   \n",
       "\n",
       "  store_primary_category  order_protocol  total_items  subtotal  \\\n",
       "0               american             1.0            4      3441   \n",
       "1                mexican             2.0            1      1900   \n",
       "2                    NaN             1.0            1      1900   \n",
       "3                    NaN             1.0            6      6900   \n",
       "4                    NaN             1.0            3      3900   \n",
       "\n",
       "   num_distinct_items  min_item_price  max_item_price  total_onshift_dashers  \\\n",
       "0                   4             557            1239                   33.0   \n",
       "1                   1            1400            1400                    1.0   \n",
       "2                   1            1900            1900                    1.0   \n",
       "3                   5             600            1800                    1.0   \n",
       "4                   3            1100            1600                    6.0   \n",
       "\n",
       "   total_busy_dashers  total_outstanding_orders  \\\n",
       "0                14.0                      21.0   \n",
       "1                 2.0                       2.0   \n",
       "2                 0.0                       0.0   \n",
       "3                 1.0                       2.0   \n",
       "4                 6.0                       9.0   \n",
       "\n",
       "   estimated_order_place_duration  \\\n",
       "0                             446   \n",
       "1                             446   \n",
       "2                             446   \n",
       "3                             446   \n",
       "4                             446   \n",
       "\n",
       "   estimated_store_to_consumer_driving_duration  \\\n",
       "0                                         861.0   \n",
       "1                                         690.0   \n",
       "2                                         690.0   \n",
       "3                                         289.0   \n",
       "4                                         650.0   \n",
       "\n",
       "   actual_total_delivery_duration  busy_dashers_ratio  \\\n",
       "0                          3779.0            0.424242   \n",
       "1                          4024.0            2.000000   \n",
       "2                          1781.0            0.000000   \n",
       "3                          3075.0            1.000000   \n",
       "4                          2390.0            1.000000   \n",
       "\n",
       "   estimated_non_prep_duration nan_free_store_primary_category  \n",
       "0                       1307.0                        american  \n",
       "1                       1136.0                          indian  \n",
       "2                       1136.0                          indian  \n",
       "3                        735.0                          indian  \n",
       "4                       1096.0                          indian  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using a custom function to fill the values\n",
    "def fill(store_id):\n",
    "    \"\"\"Return primary store category from the dictionary\"\"\"\n",
    "    try:\n",
    "        return store_id_and_category[store_id].values[0]\n",
    "    except:\n",
    "        return np.nan\n",
    "\n",
    "# fill NULL values\n",
    "historical_data['nan_free_store_primary_category'] = historical_data['store_id'].apply(fill)\n",
    "historical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 74 columns</p>\n",
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      "text/plain": [
       "   category_afghan  category_african  category_alcohol  \\\n",
       "0                0                 0                 0   \n",
       "1                0                 0                 0   \n",
       "2                0                 0                 0   \n",
       "3                0                 0                 0   \n",
       "4                0                 0                 0   \n",
       "\n",
       "   category_alcohol-plus-food  category_american  category_argentine  \\\n",
       "0                           0                  1                   0   \n",
       "1                           0                  0                   0   \n",
       "2                           0                  0                   0   \n",
       "3                           0                  0                   0   \n",
       "4                           0                  0                   0   \n",
       "\n",
       "   category_asian  category_barbecue  category_belgian  category_brazilian  \\\n",
       "0               0                  0                 0                   0   \n",
       "1               0                  0                 0                   0   \n",
       "2               0                  0                 0                   0   \n",
       "3               0                  0                 0                   0   \n",
       "4               0                  0                 0                   0   \n",
       "\n",
       "   ...  category_southern  category_spanish  category_steak  category_sushi  \\\n",
       "0  ...                  0                 0               0               0   \n",
       "1  ...                  0                 0               0               0   \n",
       "2  ...                  0                 0               0               0   \n",
       "3  ...                  0                 0               0               0   \n",
       "4  ...                  0                 0               0               0   \n",
       "\n",
       "   category_tapas  category_thai  category_turkish  category_vegan  \\\n",
       "0               0              0                 0               0   \n",
       "1               0              0                 0               0   \n",
       "2               0              0                 0               0   \n",
       "3               0              0                 0               0   \n",
       "4               0              0                 0               0   \n",
       "\n",
       "   category_vegetarian  category_vietnamese  \n",
       "0                    0                    0  \n",
       "1                    0                    0  \n",
       "2                    0                    0  \n",
       "3                    0                    0  \n",
       "4                    0                    0  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dummies for store primary category\n",
    "store_primary_category_dummies = pd.get_dummies(historical_data['nan_free_store_primary_category'])\n",
    "store_primary_category_dummies = store_primary_category_dummies.add_prefix('category_')\n",
    "store_primary_category_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's create our training dataset by introducing these new columns and removing the old ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>total_items</th>\n",
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      ],
      "text/plain": [
       "   total_items  subtotal  num_distinct_items  min_item_price  max_item_price  \\\n",
       "0            4      3441                   4             557            1239   \n",
       "1            1      1900                   1            1400            1400   \n",
       "2            1      1900                   1            1900            1900   \n",
       "3            6      6900                   5             600            1800   \n",
       "4            3      3900                   3            1100            1600   \n",
       "\n",
       "   total_onshift_dashers  total_busy_dashers  total_outstanding_orders  \\\n",
       "0                   33.0                14.0                      21.0   \n",
       "1                    1.0                 2.0                       2.0   \n",
       "2                    1.0                 0.0                       0.0   \n",
       "3                    1.0                 1.0                       2.0   \n",
       "4                    6.0                 6.0                       9.0   \n",
       "\n",
       "   estimated_order_place_duration  \\\n",
       "0                             446   \n",
       "1                             446   \n",
       "2                             446   \n",
       "3                             446   \n",
       "4                             446   \n",
       "\n",
       "   estimated_store_to_consumer_driving_duration  \\\n",
       "0                                         861.0   \n",
       "1                                         690.0   \n",
       "2                                         690.0   \n",
       "3                                         289.0   \n",
       "4                                         650.0   \n",
       "\n",
       "   actual_total_delivery_duration  busy_dashers_ratio  \\\n",
       "0                          3779.0            0.424242   \n",
       "1                          4024.0            2.000000   \n",
       "2                          1781.0            0.000000   \n",
       "3                          3075.0            1.000000   \n",
       "4                          2390.0            1.000000   \n",
       "\n",
       "   estimated_non_prep_duration  \n",
       "0                       1307.0  \n",
       "1                       1136.0  \n",
       "2                       1136.0  \n",
       "3                        735.0  \n",
       "4                       1096.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = historical_data.drop(columns=\n",
    "['created_at', 'market_id', 'store_id', 'store_primary_category', \\\n",
    "    'actual_delivery_time', 'nan_free_store_primary_category', 'order_protocol'])\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Concatenate `train_df` and the dummies\n",
    "- Convert them to `float32` (for future model's sake?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.concat([train_df, order_protocol_dummies, market_id_dummies, store_primary_category_dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_items</th>\n",
       "      <th>subtotal</th>\n",
       "      <th>num_distinct_items</th>\n",
       "      <th>min_item_price</th>\n",
       "      <th>max_item_price</th>\n",
       "      <th>total_onshift_dashers</th>\n",
       "      <th>total_busy_dashers</th>\n",
       "      <th>total_outstanding_orders</th>\n",
       "      <th>estimated_order_place_duration</th>\n",
       "      <th>estimated_store_to_consumer_driving_duration</th>\n",
       "      <th>...</th>\n",
       "      <th>category_southern</th>\n",
       "      <th>category_spanish</th>\n",
       "      <th>category_steak</th>\n",
       "      <th>category_sushi</th>\n",
       "      <th>category_tapas</th>\n",
       "      <th>category_thai</th>\n",
       "      <th>category_turkish</th>\n",
       "      <th>category_vegan</th>\n",
       "      <th>category_vegetarian</th>\n",
       "      <th>category_vietnamese</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3441.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>557.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_items  subtotal  num_distinct_items  min_item_price  max_item_price  \\\n",
       "0          4.0    3441.0                 4.0           557.0          1239.0   \n",
       "1          1.0    1900.0                 1.0          1400.0          1400.0   \n",
       "2          1.0    1900.0                 1.0          1900.0          1900.0   \n",
       "3          6.0    6900.0                 5.0           600.0          1800.0   \n",
       "4          3.0    3900.0                 3.0          1100.0          1600.0   \n",
       "\n",
       "   total_onshift_dashers  total_busy_dashers  total_outstanding_orders  \\\n",
       "0                   33.0                14.0                      21.0   \n",
       "1                    1.0                 2.0                       2.0   \n",
       "2                    1.0                 0.0                       0.0   \n",
       "3                    1.0                 1.0                       2.0   \n",
       "4                    6.0                 6.0                       9.0   \n",
       "\n",
       "   estimated_order_place_duration  \\\n",
       "0                           446.0   \n",
       "1                           446.0   \n",
       "2                           446.0   \n",
       "3                           446.0   \n",
       "4                           446.0   \n",
       "\n",
       "   estimated_store_to_consumer_driving_duration  ...  category_southern  \\\n",
       "0                                         861.0  ...                0.0   \n",
       "1                                         690.0  ...                0.0   \n",
       "2                                         690.0  ...                0.0   \n",
       "3                                         289.0  ...                0.0   \n",
       "4                                         650.0  ...                0.0   \n",
       "\n",
       "   category_spanish  category_steak  category_sushi  category_tapas  \\\n",
       "0               0.0             0.0             0.0             0.0   \n",
       "1               0.0             0.0             0.0             0.0   \n",
       "2               0.0             0.0             0.0             0.0   \n",
       "3               0.0             0.0             0.0             0.0   \n",
       "4               0.0             0.0             0.0             0.0   \n",
       "\n",
       "   category_thai  category_turkish  category_vegan  category_vegetarian  \\\n",
       "0            0.0               0.0             0.0                  0.0   \n",
       "1            0.0               0.0             0.0                  0.0   \n",
       "2            0.0               0.0             0.0                  0.0   \n",
       "3            0.0               0.0             0.0                  0.0   \n",
       "4            0.0               0.0             0.0                  0.0   \n",
       "\n",
       "   category_vietnamese  \n",
       "0                  0.0  \n",
       "1                  0.0  \n",
       "2                  0.0  \n",
       "3                  0.0  \n",
       "4                  0.0  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# align dtype over dataset\n",
    "train_df = train_df.astype('float32')\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_items</th>\n",
       "      <td>197428.0</td>\n",
       "      <td>3.196391</td>\n",
       "      <td>2.666546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subtotal</th>\n",
       "      <td>197428.0</td>\n",
       "      <td>2682.331543</td>\n",
       "      <td>1823.093750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>3395.0</td>\n",
       "      <td>27100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_distinct_items</th>\n",
       "      <td>197428.0</td>\n",
       "      <td>2.670791</td>\n",
       "      <td>1.630255</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_item_price</th>\n",
       "      <td>197428.0</td>\n",
       "      <td>686.218506</td>\n",
       "      <td>522.038635</td>\n",
       "      <td>-86.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>949.0</td>\n",
       "      <td>14700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_item_price</th>\n",
       "      <td>197428.0</td>\n",
       "      <td>1159.588623</td>\n",
       "      <td>558.411377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>14700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_thai</th>\n",
       "      <td>197428.0</td>\n",
       "      <td>0.037254</td>\n",
       "      <td>0.189384</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_turkish</th>\n",
       "      <td>197428.0</td>\n",
       "      <td>0.001175</td>\n",
       "      <td>0.034260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_vegan</th>\n",
       "      <td>197428.0</td>\n",
       "      <td>0.001433</td>\n",
       "      <td>0.037834</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_vegetarian</th>\n",
       "      <td>197428.0</td>\n",
       "      <td>0.004234</td>\n",
       "      <td>0.064935</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_vietnamese</th>\n",
       "      <td>197428.0</td>\n",
       "      <td>0.030928</td>\n",
       "      <td>0.173122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count         mean          std   min     25%     50%  \\\n",
       "total_items          197428.0     3.196391     2.666546   1.0     2.0     3.0   \n",
       "subtotal             197428.0  2682.331543  1823.093750   0.0  1400.0  2200.0   \n",
       "num_distinct_items   197428.0     2.670791     1.630255   1.0     1.0     2.0   \n",
       "min_item_price       197428.0   686.218506   522.038635 -86.0   299.0   595.0   \n",
       "max_item_price       197428.0  1159.588623   558.411377   0.0   800.0  1095.0   \n",
       "...                       ...          ...          ...   ...     ...     ...   \n",
       "category_thai        197428.0     0.037254     0.189384   0.0     0.0     0.0   \n",
       "category_turkish     197428.0     0.001175     0.034260   0.0     0.0     0.0   \n",
       "category_vegan       197428.0     0.001433     0.037834   0.0     0.0     0.0   \n",
       "category_vegetarian  197428.0     0.004234     0.064935   0.0     0.0     0.0   \n",
       "category_vietnamese  197428.0     0.030928     0.173122   0.0     0.0     0.0   \n",
       "\n",
       "                        75%      max  \n",
       "total_items             4.0    411.0  \n",
       "subtotal             3395.0  27100.0  \n",
       "num_distinct_items      3.0     20.0  \n",
       "min_item_price        949.0  14700.0  \n",
       "max_item_price       1395.0  14700.0  \n",
       "...                     ...      ...  \n",
       "category_thai           0.0      1.0  \n",
       "category_turkish        0.0      1.0  \n",
       "category_vegan          0.0      1.0  \n",
       "category_vegetarian     0.0      1.0  \n",
       "category_vietnamese     0.0      1.0  \n",
       "\n",
       "[100 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.775900e+05\n",
       "mean              NaN\n",
       "std               NaN\n",
       "min              -inf\n",
       "25%      8.269231e-01\n",
       "50%      9.622642e-01\n",
       "75%      1.000000e+00\n",
       "max               inf\n",
       "Name: busy_dashers_ratio, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['busy_dashers_ratio'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- there seems to be a division error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5,  6,  7,  9, 10, 11, 12]),)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check infinity values using numpy's isfinite() function\n",
    "np.where(np.any(~np.isfinite(train_df), axis=0) == True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(177070, 100)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace inf values with NaN for them to be easily dropped\n",
    "train_df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "train_df.dropna(inplace=True)\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collinearity and Removing Redundancies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We have 100 features, but not all of them might be useful.\n",
    "- Some features might have collinearity i.e., the variables are correlated with each other, which means that they have the same effect on the model (which is why one of them is enough in a situation where two features are extremely correlated).\n",
    "- Correlated features make it hard to interpret the model.\n",
    "- We can plot the correlation matrix of the features and study the correlation between them.\n",
    "- We can try to remove some features if they do not contribute any new knowledge to the dataset\n",
    "- A feature is deemed to be not useful if it repeats another feature or has a zero standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a mask for the upper triangle\n",
    "corr = train_df.corr()\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize = (11,9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, \\\n",
    "    vmax=3, center=0, square=True, linewidths=.5, cbar_kws={'shrink': .5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    177070.0\n",
       "mean          0.0\n",
       "std           0.0\n",
       "min           0.0\n",
       "25%           0.0\n",
       "50%           0.0\n",
       "75%           0.0\n",
       "max           0.0\n",
       "Name: category_indonesian, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['category_indonesian'].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This has a 0 std value. So it can be dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two functions to test the correlations:\n",
    "- Get redundant values\n",
    "- Find top correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_redundant_pairs(df):\n",
    "    \"\"\"Get diagonal and lower triangular pairs of correlation matrix\"\"\"\n",
    "    pairs_to_drop = set()\n",
    "    cols = df.columns\n",
    "    for i in range(0, df.shape[1]):\n",
    "        for j in range(0, i+1):\n",
    "            pairs_to_drop.add((cols[i], cols[j]))\n",
    "    return pairs_to_drop\n",
    "\n",
    "def get_top_abs_correlations(df, n=5):\n",
    "    \"\"\"Sort correlations in the descending order and return n highest results\"\"\"\n",
    "    au_corr = df.corr().abs().unstack()\n",
    "    labels_to_drop = get_redundant_pairs(df)\n",
    "    au_corr = au_corr.drop(labels=labels_to_drop).sort_values(ascending=False)\n",
    "    return au_corr[0:n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Absolute Correlations\n",
      "total_onshift_dashers                         total_busy_dashers             0.941741\n",
      "                                              total_outstanding_orders       0.934639\n",
      "total_busy_dashers                            total_outstanding_orders       0.931295\n",
      "estimated_store_to_consumer_driving_duration  estimated_non_prep_duration    0.923086\n",
      "estimated_order_place_duration                order_protocol_1.0             0.897645\n",
      "total_items                                   num_distinct_items             0.758146\n",
      "subtotal                                      num_distinct_items             0.682890\n",
      "total_items                                   subtotal                       0.557175\n",
      "min_item_price                                max_item_price                 0.541241\n",
      "subtotal                                      max_item_price                 0.507947\n",
      "order_protocol_4.0                            category_fast                  0.489946\n",
      "num_distinct_items                            min_item_price                 0.446733\n",
      "market_id_2.0                                 market_id_4.0                  0.402421\n",
      "total_items                                   min_item_price                 0.389277\n",
      "order_protocol_1.0                            order_protocol_3.0             0.373581\n",
      "estimated_order_place_duration                order_protocol_3.0             0.364170\n",
      "                                              estimated_non_prep_duration    0.363297\n",
      "order_protocol_1.0                            order_protocol_5.0             0.342345\n",
      "market_id_1.0                                 market_id_2.0                  0.334580\n",
      "estimated_order_place_duration                order_protocol_5.0             0.333291\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Top Absolute Correlations')\n",
    "print(get_top_abs_correlations(train_df, 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- total_onshift_dashers and total_busy_dashers are maxxx correlated. Drop one of them.\n",
    "- estimated_store_to_consumer_driving_duration and estimated_non_prep_duration are correlated. Drop the one we created and keep the original\n",
    "- market_ids 2 & 4 and 1 & 2 are correlated. Drop them too.\n",
    "- Re-create the train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_items</th>\n",
       "      <th>subtotal</th>\n",
       "      <th>num_distinct_items</th>\n",
       "      <th>min_item_price</th>\n",
       "      <th>max_item_price</th>\n",
       "      <th>total_outstanding_orders</th>\n",
       "      <th>estimated_order_place_duration</th>\n",
       "      <th>estimated_store_to_consumer_driving_duration</th>\n",
       "      <th>actual_total_delivery_duration</th>\n",
       "      <th>busy_dashers_ratio</th>\n",
       "      <th>...</th>\n",
       "      <th>category_southern</th>\n",
       "      <th>category_spanish</th>\n",
       "      <th>category_steak</th>\n",
       "      <th>category_sushi</th>\n",
       "      <th>category_tapas</th>\n",
       "      <th>category_thai</th>\n",
       "      <th>category_turkish</th>\n",
       "      <th>category_vegan</th>\n",
       "      <th>category_vegetarian</th>\n",
       "      <th>category_vietnamese</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3441.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>557.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3779.0</td>\n",
       "      <td>0.424242</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>4024.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>1781.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>3075.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2390.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_items  subtotal  num_distinct_items  min_item_price  max_item_price  \\\n",
       "0          4.0    3441.0                 4.0           557.0          1239.0   \n",
       "1          1.0    1900.0                 1.0          1400.0          1400.0   \n",
       "2          1.0    1900.0                 1.0          1900.0          1900.0   \n",
       "3          6.0    6900.0                 5.0           600.0          1800.0   \n",
       "4          3.0    3900.0                 3.0          1100.0          1600.0   \n",
       "\n",
       "   total_outstanding_orders  estimated_order_place_duration  \\\n",
       "0                      21.0                           446.0   \n",
       "1                       2.0                           446.0   \n",
       "2                       0.0                           446.0   \n",
       "3                       2.0                           446.0   \n",
       "4                       9.0                           446.0   \n",
       "\n",
       "   estimated_store_to_consumer_driving_duration  \\\n",
       "0                                         861.0   \n",
       "1                                         690.0   \n",
       "2                                         690.0   \n",
       "3                                         289.0   \n",
       "4                                         650.0   \n",
       "\n",
       "   actual_total_delivery_duration  busy_dashers_ratio  ...  category_southern  \\\n",
       "0                          3779.0            0.424242  ...                0.0   \n",
       "1                          4024.0            2.000000  ...                0.0   \n",
       "2                          1781.0            0.000000  ...                0.0   \n",
       "3                          3075.0            1.000000  ...                0.0   \n",
       "4                          2390.0            1.000000  ...                0.0   \n",
       "\n",
       "   category_spanish  category_steak  category_sushi  category_tapas  \\\n",
       "0               0.0             0.0             0.0             0.0   \n",
       "1               0.0             0.0             0.0             0.0   \n",
       "2               0.0             0.0             0.0             0.0   \n",
       "3               0.0             0.0             0.0             0.0   \n",
       "4               0.0             0.0             0.0             0.0   \n",
       "\n",
       "   category_thai  category_turkish  category_vegan  category_vegetarian  \\\n",
       "0            0.0               0.0             0.0                  0.0   \n",
       "1            0.0               0.0             0.0                  0.0   \n",
       "2            0.0               0.0             0.0                  0.0   \n",
       "3            0.0               0.0             0.0                  0.0   \n",
       "4            0.0               0.0             0.0                  0.0   \n",
       "\n",
       "   category_vietnamese  \n",
       "0                  0.0  \n",
       "1                  0.0  \n",
       "2                  0.0  \n",
       "3                  0.0  \n",
       "4                  0.0  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = historical_data.drop(columns = [\"created_at\", \"market_id\", \"store_id\", \"store_primary_category\", \"actual_delivery_time\", \n",
    "                                        \"nan_free_store_primary_category\", \"order_protocol\"])\n",
    "\n",
    "# don't add market_id\n",
    "train_df = pd.concat([train_df, order_protocol_dummies, store_primary_category_dummies], axis=1)\n",
    "\n",
    "# drop highly correlated features\n",
    "train_df = train_df.drop(columns=['total_onshift_dashers', 'total_busy_dashers', \\\n",
    "    'category_indonesian', 'estimated_non_prep_duration'])\n",
    "\n",
    "# align dtype over dataset\n",
    "train_df = train_df.astype('float32')\n",
    "\n",
    "# replace inf values with NaN and drop them\n",
    "train_df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "train_df.dropna(inplace=True)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(177070, 90)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Absolute Correlations\n",
      "estimated_order_place_duration  order_protocol_1.0    0.897645\n",
      "total_items                     num_distinct_items    0.758146\n",
      "subtotal                        num_distinct_items    0.682890\n",
      "total_items                     subtotal              0.557175\n",
      "min_item_price                  max_item_price        0.541241\n",
      "subtotal                        max_item_price        0.507947\n",
      "order_protocol_4.0              category_fast         0.489946\n",
      "num_distinct_items              min_item_price        0.446733\n",
      "total_items                     min_item_price        0.389277\n",
      "order_protocol_1.0              order_protocol_3.0    0.373581\n",
      "estimated_order_place_duration  order_protocol_3.0    0.364170\n",
      "order_protocol_1.0              order_protocol_5.0    0.342345\n",
      "estimated_order_place_duration  order_protocol_5.0    0.333291\n",
      "order_protocol_3.0              order_protocol_5.0    0.332533\n",
      "order_protocol_1.0              order_protocol_2.0    0.226903\n",
      "estimated_order_place_duration  order_protocol_2.0    0.221215\n",
      "order_protocol_2.0              order_protocol_3.0    0.220400\n",
      "max_item_price                  order_protocol_4.0    0.215624\n",
      "order_protocol_1.0              order_protocol_4.0    0.202151\n",
      "order_protocol_2.0              order_protocol_5.0    0.201972\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Top Absolute Correlations')\n",
    "print(get_top_abs_correlations(train_df, 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We still have a lot of correlation.\n",
    "- Look at order protocols, they're a mess. Remove them and re-run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Absolute Correlations\n",
      "total_items                                   num_distinct_items                0.758146\n",
      "subtotal                                      num_distinct_items                0.682890\n",
      "total_items                                   subtotal                          0.557175\n",
      "min_item_price                                max_item_price                    0.541241\n",
      "subtotal                                      max_item_price                    0.507947\n",
      "num_distinct_items                            min_item_price                    0.446733\n",
      "total_items                                   min_item_price                    0.389277\n",
      "estimated_store_to_consumer_driving_duration  actual_total_delivery_duration    0.188418\n",
      "subtotal                                      actual_total_delivery_duration    0.172312\n",
      "total_outstanding_orders                      estimated_order_place_duration    0.171049\n",
      "total_items                                   category_fast                     0.170988\n",
      "max_item_price                                category_italian                  0.169775\n",
      "                                              category_fast                     0.166169\n",
      "total_outstanding_orders                      actual_total_delivery_duration    0.157604\n",
      "max_item_price                                category_pizza                    0.157577\n",
      "estimated_order_place_duration                category_american                 0.150174\n",
      "min_item_price                                category_pizza                    0.149584\n",
      "subtotal                                      total_outstanding_orders          0.131115\n",
      "max_item_price                                total_outstanding_orders          0.129542\n",
      "min_item_price                                category_fast                     0.127969\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "train_df = historical_data.drop(columns = [\"created_at\", \"market_id\", \"store_id\", \"store_primary_category\", \"actual_delivery_time\", \n",
    "                                        \"nan_free_store_primary_category\", \"order_protocol\"])\n",
    "\n",
    "# don't add market_id\n",
    "train_df = pd.concat([train_df, store_primary_category_dummies], axis=1)\n",
    "\n",
    "# drop highly correlated features\n",
    "train_df = train_df.drop(columns=['total_onshift_dashers', 'total_busy_dashers', \\\n",
    "    'category_indonesian', 'estimated_non_prep_duration'])\n",
    "\n",
    "# align dtype over dataset\n",
    "train_df = train_df.astype('float32')\n",
    "\n",
    "# replace inf values with NaN and drop them\n",
    "train_df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "train_df.dropna(inplace=True)\n",
    "\n",
    "print('Top Absolute Correlations')\n",
    "print(get_top_abs_correlations(train_df, 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Still a lot of correlation.\n",
    "- But we won't drop the total_items or total distinct items as it could affect the duration of the preparation process.\n",
    "- We need to use feature engineering i.e., we will create new columns to infer the contribution of these columns.\n",
    "- **The aim is to simplify the model and increase its accuracy by using new variables as predictors.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Absolute Correlations\n",
      "min_item_price                                avg_price_per_item                0.860580\n",
      "max_item_price                                avg_price_per_item                0.770382\n",
      "min_item_price                                max_item_price                    0.541241\n",
      "total_items                                   percent_distinct_item_of_total    0.445751\n",
      "                                              min_item_price                    0.389277\n",
      "                                              avg_price_per_item                0.310759\n",
      "percent_distinct_item_of_total                avg_price_per_item                0.226711\n",
      "category_pizza                                avg_price_per_item                0.225510\n",
      "estimated_store_to_consumer_driving_duration  actual_total_delivery_duration    0.188418\n",
      "max_item_price                                percent_distinct_item_of_total    0.178021\n",
      "category_fast                                 avg_price_per_item                0.175950\n",
      "min_item_price                                percent_distinct_item_of_total    0.173524\n",
      "total_outstanding_orders                      estimated_order_place_duration    0.171049\n",
      "total_items                                   category_fast                     0.170988\n",
      "max_item_price                                category_italian                  0.169775\n",
      "                                              category_fast                     0.166169\n",
      "category_italian                              avg_price_per_item                0.158193\n",
      "total_outstanding_orders                      actual_total_delivery_duration    0.157604\n",
      "max_item_price                                category_pizza                    0.157577\n",
      "category_fast                                 percent_distinct_item_of_total    0.153614\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# new features\n",
    "train_df['percent_distinct_item_of_total'] = train_df['num_distinct_items'] / train_df['total_items']\n",
    "train_df['avg_price_per_item'] = train_df['subtotal'] / train_df['total_items']\n",
    "train_df.drop(columns=['num_distinct_items', 'subtotal'], inplace=True)\n",
    "print('Top Absolute Correlations')\n",
    "print(get_top_abs_correlations(train_df, 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- there are issues with min and max item price.\n",
    "- Create range of item price using max - min and drop both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Absolute Correlations\n",
      "total_items                                   percent_distinct_item_of_total    0.445751\n",
      "                                              price_range_of_items              0.333304\n",
      "                                              avg_price_per_item                0.310759\n",
      "percent_distinct_item_of_total                avg_price_per_item                0.226711\n",
      "category_pizza                                avg_price_per_item                0.225510\n",
      "estimated_store_to_consumer_driving_duration  actual_total_delivery_duration    0.188418\n",
      "category_fast                                 avg_price_per_item                0.175950\n",
      "total_outstanding_orders                      estimated_order_place_duration    0.171049\n",
      "total_items                                   category_fast                     0.170988\n",
      "category_italian                              avg_price_per_item                0.158193\n",
      "total_outstanding_orders                      actual_total_delivery_duration    0.157604\n",
      "category_fast                                 percent_distinct_item_of_total    0.153614\n",
      "estimated_order_place_duration                category_american                 0.150174\n",
      "category_american                             category_pizza                    0.106998\n",
      "estimated_order_place_duration                category_fast                     0.106739\n",
      "category_american                             category_mexican                  0.106459\n",
      "actual_total_delivery_duration                price_range_of_items              0.104689\n",
      "category_burger                               avg_price_per_item                0.104437\n",
      "total_outstanding_orders                      price_range_of_items              0.100228\n",
      "category_mexican                              category_pizza                    0.097818\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "train_df['price_range_of_items'] = train_df['max_item_price'] - train_df['min_item_price']\n",
    "train_df.drop(columns=['max_item_price', 'min_item_price'], inplace=True)\n",
    "print('Top Absolute Correlations')\n",
    "print(get_top_abs_correlations(train_df, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(177070, 82)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The max correlation is less than 0.5 which is good\n",
    "- We also have brought down the number of features from 100 to 82"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multicollinearity and Feature Selection\n",
    "\n",
    "### What is multi-collinearity?\n",
    "When one predictor variable in a multiple regression model can be predicted from other predictor variables, the variables are said to be multi-collinear. Essentially, multiple variables are correlated with each other. This makes it harder to interpret our model and may cause other problems like overfitting.\n",
    "\n",
    "We will use Variance Inflation Factor to quantify the **severity** of multicollinearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Compute Variance Inflation Factor (VIF) score\n",
    "- Remove features which have a VIF score > 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom function\n",
    "def compute_vif(features):\n",
    "    vif_data = pd.DataFrame()\n",
    "    vif_data['feature'] = features\n",
    "    vif_data['VIF'] = [variance_inflation_factor(train_df[features].values, i) for i in range(len(features))]\n",
    "    return vif_data.sort_values(by=['VIF']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>feature</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>category_alcohol-plus-food</td>\n",
       "      <td>1.000373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>category_chocolate</td>\n",
       "      <td>1.000508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>category_belgian</td>\n",
       "      <td>1.000771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>category_russian</td>\n",
       "      <td>1.003245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>category_african</td>\n",
       "      <td>1.003825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>busy_dashers_ratio</td>\n",
       "      <td>6.369577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>category_american</td>\n",
       "      <td>7.033606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>estimated_store_to_consumer_driving_duration</td>\n",
       "      <td>7.210893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>estimated_order_place_duration</td>\n",
       "      <td>13.470942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>percent_distinct_item_of_total</td>\n",
       "      <td>30.336609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         feature        VIF\n",
       "0                     category_alcohol-plus-food   1.000373\n",
       "1                             category_chocolate   1.000508\n",
       "2                               category_belgian   1.000771\n",
       "3                               category_russian   1.003245\n",
       "4                               category_african   1.003825\n",
       "..                                           ...        ...\n",
       "76                            busy_dashers_ratio   6.369577\n",
       "77                             category_american   7.033606\n",
       "78  estimated_store_to_consumer_driving_duration   7.210893\n",
       "79                estimated_order_place_duration  13.470942\n",
       "80                percent_distinct_item_of_total  30.336609\n",
       "\n",
       "[81 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply VIF computation to all columns\n",
    "features = train_df.drop(columns=['actual_total_delivery_duration']).columns.to_list()\n",
    "vif_data = compute_vif(features)\n",
    "vif_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks like `percent_distinct_item_of_total` is the only feature with VIF > 20\n",
    "- We will remove it and also check for multicollinearity in the remaining features and repeat the process\n",
    "- For this, we need a loop; here we're using a `while` loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing  percent_distinct_item_of_total\n"
     ]
    },
    {
     "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>feature</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>category_alcohol-plus-food</td>\n",
       "      <td>1.000224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>category_chocolate</td>\n",
       "      <td>1.000377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>category_belgian</td>\n",
       "      <td>1.000472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>category_russian</td>\n",
       "      <td>1.002037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>category_gluten-free</td>\n",
       "      <td>1.002381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>category_american</td>\n",
       "      <td>4.505930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>avg_price_per_item</td>\n",
       "      <td>5.958989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>busy_dashers_ratio</td>\n",
       "      <td>6.357856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>estimated_store_to_consumer_driving_duration</td>\n",
       "      <td>7.192701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>estimated_order_place_duration</td>\n",
       "      <td>13.338060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         feature        VIF\n",
       "0                     category_alcohol-plus-food   1.000224\n",
       "1                             category_chocolate   1.000377\n",
       "2                               category_belgian   1.000472\n",
       "3                               category_russian   1.002037\n",
       "4                           category_gluten-free   1.002381\n",
       "..                                           ...        ...\n",
       "75                             category_american   4.505930\n",
       "76                            avg_price_per_item   5.958989\n",
       "77                            busy_dashers_ratio   6.357856\n",
       "78  estimated_store_to_consumer_driving_duration   7.192701\n",
       "79                estimated_order_place_duration  13.338060\n",
       "\n",
       "[80 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multicollinearity = True\n",
    "\n",
    "while multicollinearity:\n",
    "    highest_vf_feature = vif_data['feature'].values.tolist()[-1] # since VIF is in ascending order\n",
    "    print(\"Removing \", highest_vf_feature)\n",
    "    features.remove(highest_vf_feature)\n",
    "    vif_data = compute_vif(features)\n",
    "    multicollinearity = False if len(vif_data[vif_data['VIF'] > 20]) == 0 else True\n",
    "\n",
    "selected_features = vif_data['feature'].values.tolist()\n",
    "vif_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It was enough to just drop `percent_distinct_item_of_total`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "### Rationale:\n",
    "- Feature selection works to reduce the dimensions of a dataset, and getting rid of the features that don't have a significant effect on the model. It also helps our algorithm to run faster.\n",
    "- Two popular ways to do feature selection are **PCA** and **Random Forest Regressors**\n",
    "- RFR gives out the Gini-Index importance of each feature\n",
    "### Gini Index:\n",
    "- Gini index is a measure of impurity used for splitting nodes in the decision trees that make up the Random Forest\n",
    "- It quantifies the degree of class mixing within a node by calculating the probability of misclassifying a randomly chosen sample in the node.\n",
    "- The Gini index is minimized when a node contains pure samples of a single class, and it is used as a criterion to determine the optimal splits in the decision trees during the construction of the random forest.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# selected features are selected in multicollinearity check part\n",
    "X = train_df[selected_features]\n",
    "y = train_df['actual_total_delivery_duration']\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = [f\"feature {i}\" for i in range((X.shape[1]))]\n",
    "forest = RandomForestRegressor(random_state=42)\n",
    "forest.fit(X_train, y_train)\n",
    "feats = {} # a dict to hold feature_name: feature_importance\n",
    "for feature, importance in zip(X.columns, forest.feature_importances_):\n",
    "    feats[feature] = importance # add the name/value pair\n",
    "importances = pd.DataFrame.from_dict(feats, orient = 'index').rename(columns={0: 'Gini-importance'})\n",
    "importances.sort_values(by='Gini-importance').plot(kind='bar', rot=90, figsize=(15, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the most important ones\n",
    "importances.sort_values(by='Gini-importance')[-35:].plot(kind='bar', figsize=(15,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['category_middle-eastern',\n",
       " 'category_hawaiian',\n",
       " 'category_catering',\n",
       " 'category_smoothie',\n",
       " 'category_sushi',\n",
       " 'category_asian',\n",
       " 'category_barbecue',\n",
       " 'category_alcohol',\n",
       " 'category_greek',\n",
       " 'category_seafood',\n",
       " 'category_cafe',\n",
       " 'category_other',\n",
       " 'category_salad',\n",
       " 'category_vietnamese',\n",
       " 'category_breakfast',\n",
       " 'category_italian',\n",
       " 'category_fast',\n",
       " 'category_sandwich',\n",
       " 'category_chinese',\n",
       " 'category_thai',\n",
       " 'category_mediterranean',\n",
       " 'category_japanese',\n",
       " 'category_dessert',\n",
       " 'category_mexican',\n",
       " 'category_indian',\n",
       " 'category_burger',\n",
       " 'category_american',\n",
       " 'category_pizza',\n",
       " 'estimated_order_place_duration',\n",
       " 'total_items',\n",
       " 'price_range_of_items',\n",
       " 'busy_dashers_ratio',\n",
       " 'total_outstanding_orders',\n",
       " 'avg_price_per_item',\n",
       " 'estimated_store_to_consumer_driving_duration']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obtaining the columns names as text\n",
    "importances.sort_values(by='Gini-importance')[-35:].index.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking feature importance using PCA\n",
    "### Rationale:\n",
    "- PCA can help determine the number of features required to explain a certain percentage of data.\n",
    "- For eg., 50 features are required to represent 60% of the data.\n",
    "- We can set a threshold on the percentage explainability and determine the number of features required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wg/8yz1tny10yl9ct8bq7zl3pp00000gn/T/ipykernel_13359/1689002806.py:11: MatplotlibDeprecationWarning: Passing the emit parameter of set_xlim() positionally is deprecated since Matplotlib 3.6; the parameter will become keyword-only two minor releases later.\n",
      "  plt.xlim(0,81,1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X_Train = X_train.values # changing from t to T\n",
    "X_Train = np.asarray(X_Train)\n",
    "\n",
    "# Finding normalised array of X_train\n",
    "X_std = StandardScaler().fit_transform(X_Train)\n",
    "pca = PCA().fit(X_std)\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlim(0,81,1)\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Cumulative explained variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- PCA shows that we need to use at least 60 representative features to explain 80% of the dataset, which makes the PCA transformation useless since we already have 80 and could select the most important ones based on feature importance.\n",
    "- However, if PCA would tell us it can explain the majority of variance with around 10 features - high reduction - we would continue with it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling\n",
    "### Rationale:\n",
    "- For developing a model for further interpretation, scaling is important to ensure that feature values are proportionally adjusted to fit within a specified range (typically between 0 and 1)\n",
    "- Effect of scaling on Gradient Descent: When features have different scales, gradient descent may take longer to converge or not converge at all. Feature scaling such as normalization or standardization, ensures that all features are on a similar scale, allowing gradient descent to avoid disporoportionately large updates based on features with large values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will try two Scalers\n",
    "- MinMax Scaler (Normalization)\n",
    "- Standard Scaler (Standardization)\n",
    "    - Makes the mean 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom function which will work for both scalers\n",
    "\n",
    "def scale(scaler, X, y):\n",
    "    \"\"\"Apply the selected scaler to features and target variables\"\"\"\n",
    "    X_scaler = scaler\n",
    "    X_scaler.fit(X=X, y=y)\n",
    "    X_scaled = X_scaler.transform(X)\n",
    "\n",
    "    y_scaler = scaler\n",
    "    y_scaler.fit(y.values.reshape(-1,1))\n",
    "    y_scaled = y_scaler.transform(y.values.reshape(-1,1))\n",
    "\n",
    "    return X_scaled, y_scaled, X_scaler, y_scaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example to show how to use it\n",
    "X_scaled, y_scaled, X_scaler, y_scaler = scale(MinMaxScaler(), X, y)\n",
    "\n",
    "# train-test split after scaling\n",
    "X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled = train_test_split(\n",
    "    X_scaled, y_scaled, test_size=0.2, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inverse Transform for Metric Comparison\n",
    "- Since we applied a scaler to training data, the predictions for test data will also be scaled.\n",
    "- And to see the error in terms of seconds, we need to apply the inverse transformation.\n",
    "- We will use Root Mean Squared Error to measure error. We could also use Mean Squared Error or Mean Absolute Error.\n",
    "- We gave our decision based on the sensitivity of RMSE for high error terms. In our thought, the consumer patience with delaying delivery could decrease exponentially with time.\n",
    "- The detailed comparison of MAE, MSE, and, RMSE could be found here: https://towardsdatascience.com/comparing-robustness-of-mae-mse-and-rmse-6d69da870828"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def rmse_with_inv_transform(scaler, y_test, y_pred_scaled, model_name):\n",
    "    \"\"\"Convert the scaled error to actual error\"\"\"\n",
    "    y_predict = scaler.inverse_transform(y_pred_scaled.reshape(-1,1))\n",
    "    # return RMSE with squared False\n",
    "    rmse_error = mean_squared_error(y_test, y_predict[:,0], squared=False) # squared : bool, default=True. If True returns MSE value, if False returns RMSE value.\n",
    "    print(\"Error = \"'{}'.format(rmse_error) + \" in \" + model_name)\n",
    "\n",
    "    return rmse_error, y_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classical Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We will:\n",
    "    - Apply 6 different algorithms to help us find the best performance model.\n",
    "    - Apply 4 different feature set sizes - full, 40, 20 and 10 (selected by Gini importance)\n",
    "    - Use 3 different scalers - Standard, MinMax and No Scalar.\n",
    "- We will get 6 x 4 x 3 = 72 results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn import tree, svm, neighbors, linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a generic function which can work with multiple ML models\n",
    "\n",
    "def make_regression(X_train, y_train, X_test, y_test, model, model_name, verbose=True):\n",
    "    \"\"\"Apply selected regression model to data and measure error\"\"\"\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    y_predict = model.predict(X_train) # predictions on the training data itself, not test data\n",
    "    train_error = mean_squared_error(y_train, y_predict, squared=False)\n",
    "    \n",
    "    y_predict = model.predict(X_test) # predictions on the test data\n",
    "    test_error = mean_squared_error(y_test, y_predict, squared=False)\n",
    "\n",
    "    if verbose:\n",
    "        print(\"Train Error = \"'{}'.format(train_error) + \" \" + model_name)\n",
    "        print(\"Test Error = \"'{}'.format(test_error) + \" \" + model_name)\n",
    "    trained_model = model\n",
    "\n",
    "    return trained_model, y_predict, train_error, test_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We will now apply several regression tasks and calculate error for each task\n",
    "- We will collect the results in 4 different dictionaries as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_dict = {\n",
    "    'regression_model': [],\n",
    "    'feature_set': [],\n",
    "    'scaler_name': [],\n",
    "    'RMSE': [],\n",
    "}\n",
    "\n",
    "regression_models = {\n",
    "    'Ridge': linear_model.Ridge(),\n",
    "    'DecisionTree': tree.DecisionTreeRegressor(max_depth=6),\n",
    "    'RandomForest': RandomForestRegressor(),\n",
    "    'XGBoost': XGBRegressor(),\n",
    "    'LGBM': LGBMRegressor(),\n",
    "    'MLP': MLPRegressor(), # Multi-Layer Perceptron\n",
    "}\n",
    "\n",
    "feature_sets = {\n",
    "    'full_dataset': X.columns.to_list(),\n",
    "    'selected_features_40': importances.sort_values(by='Gini-importance')[-40:].index.tolist(),\n",
    "    'selected_features_20': importances.sort_values(by='Gini-importance')[-20:].index.tolist(),\n",
    "    'selected_features_10': importances.sort_values(by='Gini-importance')[-10:].index.tolist(),\n",
    "}\n",
    "\n",
    "scalers = {\n",
    "    'Standard scaler': StandardScaler(),\n",
    "    'MinMax scaler': MinMaxScaler(),\n",
    "    'NotScale': None,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----scaled with Standard scaler----- included columns are full_dataset\n",
      "\n",
      "Error = 2053.69873046875 in Ridge\n",
      "Error = 2057.2476687088733 in DecisionTree\n",
      "Error = 2048.8879893573203 in RandomForest\n",
      "Error = 2036.2498779296875 in XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.016542 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1537\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 70\n",
      "[LightGBM] [Info] Start training from score -0.002379\n",
      "Error = 2033.4355809987908 in LGBM\n",
      "Error = 2036.7374267578125 in MLP\n",
      "-----scaled with MinMax scaler----- included columns are full_dataset\n",
      "\n",
      "Error = 2053.962890625 in Ridge\n",
      "Error = 2057.2476685732177 in DecisionTree\n",
      "Error = 2047.0233950790237 in RandomForest\n",
      "Error = 2035.351318359375 in XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001505 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1471\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 70\n",
      "[LightGBM] [Info] Start training from score 0.008275\n",
      "Error = 2034.4499976619875 in LGBM\n",
      "Error = 2167.79931640625 in MLP\n",
      "-----scaled with NotScale----- included columns are full_dataset\n",
      "\n",
      "Train Error = 1071.4970703125 Ridge\n",
      "Test Error = 2053.692626953125 Ridge\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:212: LinAlgWarning: Ill-conditioned matrix (rcond=4.4253e-11): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Error = 1067.7815291116704 DecisionTree\n",
      "Test Error = 2057.2476684096064 DecisionTree\n",
      "Train Error = 396.76158853297966 RandomForest\n",
      "Test Error = 2047.9909015580602 RandomForest\n",
      "Train Error = 968.2471313476562 XGBoost\n",
      "Test Error = 2036.43212890625 XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001528 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1472\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 70\n",
      "[LightGBM] [Info] Start training from score 2851.342096\n",
      "Train Error = 1017.5549840201221 LGBM\n",
      "Test Error = 2034.461097275308 LGBM\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Error = 1054.0926513671875 MLP\n",
      "Test Error = 2046.8621826171875 MLP\n",
      "-----scaled with Standard scaler----- included columns are selected_features_40\n",
      "\n",
      "Error = 2054.00439453125 in Ridge\n",
      "Error = 2056.5292024696128 in DecisionTree\n",
      "Error = 2045.519267792819 in RandomForest\n",
      "Error = 2035.453369140625 in XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011227 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1447\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 40\n",
      "[LightGBM] [Info] Start training from score -0.002379\n",
      "Error = 2034.6538984910121 in LGBM\n",
      "Error = 2036.6802978515625 in MLP\n",
      "-----scaled with MinMax scaler----- included columns are selected_features_40\n",
      "\n",
      "Error = 2054.27734375 in Ridge\n",
      "Error = 2056.5292023632996 in DecisionTree\n",
      "Error = 2048.06295701124 in RandomForest\n",
      "Error = 2034.2847900390625 in XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002023 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1411\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 40\n",
      "[LightGBM] [Info] Start training from score 0.008275\n",
      "Error = 2035.196536738781 in LGBM\n",
      "Error = 2119.08984375 in MLP\n",
      "-----scaled with NotScale----- included columns are selected_features_40\n",
      "\n",
      "Train Error = 1072.35498046875 Ridge\n",
      "Test Error = 2054.00439453125 Ridge\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:212: LinAlgWarning: Ill-conditioned matrix (rcond=2.55922e-09): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Error = 1067.785563647774 DecisionTree\n",
      "Test Error = 2056.529202224364 DecisionTree\n",
      "Train Error = 399.7917753724075 RandomForest\n",
      "Test Error = 2049.50877369409 RandomForest\n",
      "Train Error = 965.6858520507812 XGBoost\n",
      "Test Error = 2035.7642822265625 XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001895 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1412\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 40\n",
      "[LightGBM] [Info] Start training from score 2851.342096\n",
      "Train Error = 1017.1430421117441 LGBM\n",
      "Test Error = 2034.8058530174258 LGBM\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Error = 1056.1436767578125 MLP\n",
      "Test Error = 2048.456298828125 MLP\n",
      "-----scaled with Standard scaler----- included columns are selected_features_20\n",
      "\n",
      "Error = 2055.36572265625 in Ridge\n",
      "Error = 2054.610311207463 in DecisionTree\n",
      "Error = 2049.6485727298664 in RandomForest\n",
      "Error = 2037.6336669921875 in XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007529 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1387\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 20\n",
      "[LightGBM] [Info] Start training from score -0.002379\n",
      "Error = 2036.0233846729805 in LGBM\n",
      "Error = 2038.553466796875 in MLP\n",
      "-----scaled with MinMax scaler----- included columns are selected_features_20\n",
      "\n",
      "Error = 2055.653076171875 in Ridge\n",
      "Error = 2054.6103112689557 in DecisionTree\n",
      "Error = 2046.9187547613492 in RandomForest\n",
      "Error = 2036.5164794921875 in XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001443 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1371\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 20\n",
      "[LightGBM] [Info] Start training from score 0.008275\n",
      "Error = 2035.581516471472 in LGBM\n",
      "Error = 2094.78955078125 in MLP\n",
      "-----scaled with NotScale----- included columns are selected_features_20\n",
      "\n",
      "Train Error = 1074.506591796875 Ridge\n",
      "Test Error = 2055.36572265625 Ridge\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:212: LinAlgWarning: Ill-conditioned matrix (rcond=4.43857e-08): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Error = 1069.1350906055598 DecisionTree\n",
      "Test Error = 2054.610311170708 DecisionTree\n",
      "Train Error = 397.60170078512033 RandomForest\n",
      "Test Error = 2049.9557303482325 RandomForest\n",
      "Train Error = 966.1651000976562 XGBoost\n",
      "Test Error = 2036.249267578125 XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001811 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1372\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 20\n",
      "[LightGBM] [Info] Start training from score 2851.342096\n",
      "Train Error = 1018.9600610110249 LGBM\n",
      "Test Error = 2035.9494403356318 LGBM\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Error = 1057.6063232421875 MLP\n",
      "Test Error = 2048.77783203125 MLP\n",
      "-----scaled with Standard scaler----- included columns are selected_features_10\n",
      "\n",
      "Error = 2056.568359375 in Ridge\n",
      "Error = 2054.5971761770547 in DecisionTree\n",
      "Error = 2052.0113204538134 in RandomForest\n",
      "Error = 2040.27001953125 in XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005490 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1357\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 10\n",
      "[LightGBM] [Info] Start training from score -0.002379\n",
      "Error = 2038.1313604554389 in LGBM\n",
      "Error = 2039.998046875 in MLP\n",
      "-----scaled with MinMax scaler----- included columns are selected_features_10\n",
      "\n",
      "Error = 2056.882080078125 in Ridge\n",
      "Error = 2054.5971762373274 in DecisionTree\n",
      "Error = 2051.2262801562138 in RandomForest\n",
      "Error = 2039.515625 in XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006372 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1351\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 10\n",
      "[LightGBM] [Info] Start training from score 0.008275\n",
      "Error = 2037.6564489201091 in LGBM\n",
      "Error = 2069.63525390625 in MLP\n",
      "-----scaled with NotScale----- included columns are selected_features_10\n",
      "\n",
      "Train Error = 1076.7279052734375 Ridge\n",
      "Test Error = 2056.568359375 Ridge\n",
      "Train Error = 1069.2324011708552 DecisionTree\n",
      "Test Error = 2054.597176140608 DecisionTree\n",
      "Train Error = 398.07374627567987 RandomForest\n",
      "Test Error = 2052.000421456383 RandomForest\n",
      "Train Error = 976.43798828125 XGBoost\n",
      "Test Error = 2039.2135009765625 XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005149 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1352\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 10\n",
      "[LightGBM] [Info] Start training from score 2851.342096\n",
      "Train Error = 1022.939378192528 LGBM\n",
      "Test Error = 2038.4873761329577 LGBM\n",
      "Train Error = 1061.3724365234375 MLP\n",
      "Test Error = 2050.49609375 MLP\n"
     ]
    }
   ],
   "source": [
    "# examine the error for each combination\n",
    "\n",
    "for feature_set_name in feature_sets.keys():\n",
    "    feature_set = feature_sets[feature_set_name]\n",
    "    for scaler_name in scalers.keys():\n",
    "        print(f\"-----scaled with {scaler_name}----- included columns are {feature_set_name}\")\n",
    "        print(\"\")\n",
    "        for model_name in regression_models.keys():\n",
    "            if scaler_name == \"NotScale\":\n",
    "                X = train_df[feature_set]\n",
    "                y = train_df['actual_total_delivery_duration']\n",
    "                X_train, X_test, y_train, y_test = train_test_split(\n",
    "                    X, y, test_size=0.2, random_state=42\n",
    "                )\n",
    "                make_regression(X_train, y_train, X_test, y_test, regression_models[model_name], \\\n",
    "                    model_name, verbose=True)\n",
    "            else:\n",
    "                X_scaled, y_scaled, X_scaler, y_scaler = scale(scalers[scaler_name], X[feature_set], y)\n",
    "                X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled = train_test_split(\n",
    "                    X_scaled, y_scaled, test_size = 0.2, random_state=42\n",
    "                )\n",
    "                _, y_predict_scaled, _, _ = make_regression(X_train_scaled, y_train_scaled[:,0], \\\n",
    "                    X_test_scaled, y_test_scaled[:,0], regression_models[model_name], model_name, \\\n",
    "                        verbose=False)\n",
    "                rmse_error, y_predict = rmse_with_inv_transform(y_scaler, y_test,  y_predict_scaled, \\\n",
    "                    model_name)\n",
    "            \n",
    "            pred_dict['regression_model'].append(model_name)\n",
    "            pred_dict['feature_set'].append(feature_set_name)\n",
    "            pred_dict['scaler_name'].append(scaler_name)\n",
    "            pred_dict['RMSE'].append(rmse_error)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>regression_model</th>\n",
       "      <th>feature_set</th>\n",
       "      <th>scaler_name</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>full_dataset</td>\n",
       "      <td>Standard scaler</td>\n",
       "      <td>2053.698730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>full_dataset</td>\n",
       "      <td>Standard scaler</td>\n",
       "      <td>2057.247669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>full_dataset</td>\n",
       "      <td>Standard scaler</td>\n",
       "      <td>2048.887989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>full_dataset</td>\n",
       "      <td>Standard scaler</td>\n",
       "      <td>2036.249878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LGBM</td>\n",
       "      <td>full_dataset</td>\n",
       "      <td>Standard scaler</td>\n",
       "      <td>2033.435581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>selected_features_10</td>\n",
       "      <td>NotScale</td>\n",
       "      <td>2069.635254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>selected_features_10</td>\n",
       "      <td>NotScale</td>\n",
       "      <td>2069.635254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>selected_features_10</td>\n",
       "      <td>NotScale</td>\n",
       "      <td>2069.635254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>LGBM</td>\n",
       "      <td>selected_features_10</td>\n",
       "      <td>NotScale</td>\n",
       "      <td>2069.635254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>MLP</td>\n",
       "      <td>selected_features_10</td>\n",
       "      <td>NotScale</td>\n",
       "      <td>2069.635254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   regression_model           feature_set      scaler_name         RMSE\n",
       "0             Ridge          full_dataset  Standard scaler  2053.698730\n",
       "1      DecisionTree          full_dataset  Standard scaler  2057.247669\n",
       "2      RandomForest          full_dataset  Standard scaler  2048.887989\n",
       "3           XGBoost          full_dataset  Standard scaler  2036.249878\n",
       "4              LGBM          full_dataset  Standard scaler  2033.435581\n",
       "..              ...                   ...              ...          ...\n",
       "67     DecisionTree  selected_features_10         NotScale  2069.635254\n",
       "68     RandomForest  selected_features_10         NotScale  2069.635254\n",
       "69          XGBoost  selected_features_10         NotScale  2069.635254\n",
       "70             LGBM  selected_features_10         NotScale  2069.635254\n",
       "71              MLP  selected_features_10         NotScale  2069.635254\n",
       "\n",
       "[72 rows x 4 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df = pd.DataFrame(pred_dict)\n",
    "pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_df.plot(kind='bar', figsize=(15,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It seems we have high errors in all models.\n",
    "- Also, the models inform us that to not scale affects the performance negatively. We could pick one of the scalers and continue since it doesn't affect the performance much.\n",
    "- The decrease in training feature size first slightly improves the performance and then marginally made it worse. We choose to use 40 as a final decision.\n",
    "- When we examine the model performances, the most significant thing is the error rates. What could we do more to improve it?\n",
    "- Could it be our dataset is **not a good representative** for estimating the total delivery duration?\n",
    "\n",
    "## Changing the problem a bit\n",
    "Let's predict the prep_duration (a created variable) which will the difference between `actual_total_delivery_duration` and the non-prep activities duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----scaled with Standard Scaler----- included columns are selected_features_40\n",
      "\n",
      "Error = 2071.3193359375 in Ridge\n",
      "Error = 2064.1583962635023 in DecisionTree\n",
      "Error = 2068.1194307071405 in RandomForest\n",
      "Error = 2053.066162109375 in XGBoost\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011127 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1163\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 38\n",
      "[LightGBM] [Info] Start training from score -0.002552\n",
      "Error = 2052.763010500947 in LGBM\n",
      "Error = 2055.383056640625 in MLP\n"
     ]
    }
   ],
   "source": [
    "train_df['prep_time'] = train_df['actual_total_delivery_duration'] \\\n",
    "    - train_df['estimated_order_place_duration'] - train_df['estimated_order_place_duration']\n",
    "\n",
    "scalers = {\n",
    "    'Standard Scaler': StandardScaler(),\n",
    "}\n",
    "\n",
    "feature_sets = {\n",
    "    'selected_features_40': importances.sort_values(by='Gini-importance')[-40:].index.tolist()\n",
    "}\n",
    "\n",
    "for feature_set_name in feature_sets.keys():\n",
    "    feature_set = feature_sets[feature_set_name]\n",
    "    for scaler_name in scalers.keys():\n",
    "        print(f\"-----scaled with {scaler_name}----- included columns are {feature_set_name}\")\n",
    "        print(\"\")\n",
    "        for model_name in regression_models.keys():\n",
    "            # drop estimated_store_to_consumer_driving_duration and estimated_order_place_duration\n",
    "            X = train_df[feature_set].drop(columns=['estimated_store_to_consumer_driving_duration', \\\n",
    "                'estimated_order_place_duration'])\n",
    "            y = train_df['prep_time']\n",
    "\n",
    "            # to get indices\n",
    "            X_train, X_test, y_train, y_test = train_test_split(\n",
    "                X, y, test_size=0.2, random_state=42\n",
    "            )\n",
    "            train_indices = X_train.index\n",
    "            test_indices = X_test.index\n",
    "\n",
    "            # scale\n",
    "            X_scaled, y_scaled, X_scaler, y_scaler = scale(scalers[scaler_name], X, y)\n",
    "\n",
    "            # apply indexing\n",
    "            X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled = train_test_split(\n",
    "                X_scaled, y_scaled, test_size=0.2, random_state=42\n",
    "            )\n",
    "            _, y_predict_scaled, _, _ = make_regression(X_train_scaled, y_train_scaled[:,0], X_test_scaled, \\\n",
    "                y_test_scaled[:,0], regression_models[model_name], model_name, verbose=False)\n",
    "            rmse_error, y_predict = rmse_with_inv_transform(y_scaler, y_test, y_predict_scaled, model_name)\n",
    "\n",
    "            pred_dict['regression_model'].append(model_name)\n",
    "            pred_dict['feature_set'].append(feature_set_name)\n",
    "            pred_dict['scaler_name'].append(scaler_name)\n",
    "            pred_dict['RMSE'].append(rmse_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- LGBM performs the best among all models.\n",
    "- Repeat the process just for that model alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----scaled with Standard Scaler----- included columns are selected_features_40\n",
      "\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010671 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1163\n",
      "[LightGBM] [Info] Number of data points in the train set: 141656, number of used features: 38\n",
      "[LightGBM] [Info] Start training from score -0.002552\n",
      "Error = 2052.763010500947 in LGBM\n"
     ]
    }
   ],
   "source": [
    "scalers = {\n",
    "    'Standard Scaler': StandardScaler(),\n",
    "}\n",
    "\n",
    "feature_sets = {\n",
    "    'selected_features_40': importances.sort_values(by='Gini-importance')[-40:].index.tolist()\n",
    "}\n",
    "\n",
    "regression_models = {\n",
    "    'LGBM': LGBMRegressor(),\n",
    "}\n",
    "\n",
    "for feature_set_name in feature_sets.keys():\n",
    "    feature_set = feature_sets[feature_set_name]\n",
    "    for scaler_name in scalers.keys():\n",
    "        print(f\"-----scaled with {scaler_name}----- included columns are {feature_set_name}\")\n",
    "        print(\"\")\n",
    "        for model_name in regression_models.keys():\n",
    "            # drop estimated_store_to_consumer_driving_duration and estimated_order_place_duration\n",
    "            X = train_df[feature_set].drop(columns=['estimated_store_to_consumer_driving_duration', \\\n",
    "                'estimated_order_place_duration'])\n",
    "            y = train_df['prep_time']\n",
    "\n",
    "            # to get indices\n",
    "            X_train, X_test, y_train, y_test = train_test_split(\n",
    "                X, y, test_size=0.2, random_state=42\n",
    "            )\n",
    "            train_indices = X_train.index\n",
    "            test_indices = X_test.index\n",
    "\n",
    "            # scale\n",
    "            X_scaled, y_scaled, X_scaler, y_scaler = scale(scalers[scaler_name], X, y)\n",
    "\n",
    "            # apply indexing\n",
    "            X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled = train_test_split(\n",
    "                X_scaled, y_scaled, test_size=0.2, random_state=42\n",
    "            )\n",
    "            _, y_predict_scaled, _, _ = make_regression(X_train_scaled, y_train_scaled[:,0], X_test_scaled, \\\n",
    "                y_test_scaled[:,0], regression_models[model_name], model_name, verbose=False)\n",
    "            rmse_error, y_predict = rmse_with_inv_transform(y_scaler, y_test, y_predict_scaled, model_name)\n",
    "\n",
    "            pred_dict['regression_model'].append(model_name)\n",
    "            pred_dict['feature_set'].append(feature_set_name)\n",
    "            pred_dict['scaler_name'].append(scaler_name)\n",
    "            pred_dict['RMSE'].append(rmse_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excracting Predictions from the best performing model\n",
    "pred_values_dict = {\n",
    "    'actual_total_delivery_duration': train_df['actual_total_delivery_duration'][test_indices].values.tolist(),\n",
    "    'prep_duration_prediction': y_predict[:,0].tolist(),\n",
    "    'estimated_store_to_consumer_driving_duration': train_df['estimated_store_to_consumer_driving_duration'] \\\n",
    "        [test_indices].values.tolist(),\n",
    "    'estimated_order_place_duration': train_df['estimated_order_place_duration'][test_indices].values.tolist(),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>actual_total_delivery_duration</th>\n",
       "      <th>prep_duration_prediction</th>\n",
       "      <th>estimated_store_to_consumer_driving_duration</th>\n",
       "      <th>estimated_order_place_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2448.0</td>\n",
       "      <td>1929.168417</td>\n",
       "      <td>380.0</td>\n",
       "      <td>251.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1839.0</td>\n",
       "      <td>1682.649684</td>\n",
       "      <td>747.0</td>\n",
       "      <td>446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1927.0</td>\n",
       "      <td>2376.807852</td>\n",
       "      <td>543.0</td>\n",
       "      <td>251.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>2657.0</td>\n",
       "      <td>1531.337771</td>\n",
       "      <td>445.0</td>\n",
       "      <td>251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3596.0</td>\n",
       "      <td>2237.349571</td>\n",
       "      <td>691.0</td>\n",
       "      <td>446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>35409</th>\n",
       "      <td>9557.0</td>\n",
       "      <td>2932.175128</td>\n",
       "      <td>768.0</td>\n",
       "      <td>446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35410</th>\n",
       "      <td>1864.0</td>\n",
       "      <td>2373.824959</td>\n",
       "      <td>324.0</td>\n",
       "      <td>446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35411</th>\n",
       "      <td>2674.0</td>\n",
       "      <td>2093.882146</td>\n",
       "      <td>791.0</td>\n",
       "      <td>251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35412</th>\n",
       "      <td>2234.0</td>\n",
       "      <td>2163.224374</td>\n",
       "      <td>687.0</td>\n",
       "      <td>251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35413</th>\n",
       "      <td>1441.0</td>\n",
       "      <td>1980.095307</td>\n",
       "      <td>452.0</td>\n",
       "      <td>446.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35414 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual_total_delivery_duration  prep_duration_prediction  \\\n",
       "0                              2448.0               1929.168417   \n",
       "1                              1839.0               1682.649684   \n",
       "2                              1927.0               2376.807852   \n",
       "3                              2657.0               1531.337771   \n",
       "4                              3596.0               2237.349571   \n",
       "...                               ...                       ...   \n",
       "35409                          9557.0               2932.175128   \n",
       "35410                          1864.0               2373.824959   \n",
       "35411                          2674.0               2093.882146   \n",
       "35412                          2234.0               2163.224374   \n",
       "35413                          1441.0               1980.095307   \n",
       "\n",
       "       estimated_store_to_consumer_driving_duration  \\\n",
       "0                                             380.0   \n",
       "1                                             747.0   \n",
       "2                                             543.0   \n",
       "3                                             445.0   \n",
       "4                                             691.0   \n",
       "...                                             ...   \n",
       "35409                                         768.0   \n",
       "35410                                         324.0   \n",
       "35411                                         791.0   \n",
       "35412                                         687.0   \n",
       "35413                                         452.0   \n",
       "\n",
       "       estimated_order_place_duration  \n",
       "0                               251.0  \n",
       "1                               446.0  \n",
       "2                               251.0  \n",
       "3                               251.0  \n",
       "4                               446.0  \n",
       "...                               ...  \n",
       "35409                           446.0  \n",
       "35410                           446.0  \n",
       "35411                           251.0  \n",
       "35412                           251.0  \n",
       "35413                           446.0  \n",
       "\n",
       "[35414 rows x 4 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting to dataframe from dict\n",
    "values_df = pd.DataFrame.from_dict(pred_values_dict)\n",
    "values_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual_total_delivery_duration</th>\n",
       "      <th>prep_duration_prediction</th>\n",
       "      <th>estimated_store_to_consumer_driving_duration</th>\n",
       "      <th>estimated_order_place_duration</th>\n",
       "      <th>sum_total_delivery_direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2448.0</td>\n",
       "      <td>1929.168417</td>\n",
       "      <td>380.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>2560.168417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1839.0</td>\n",
       "      <td>1682.649684</td>\n",
       "      <td>747.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>2875.649684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1927.0</td>\n",
       "      <td>2376.807852</td>\n",
       "      <td>543.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>3170.807852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2657.0</td>\n",
       "      <td>1531.337771</td>\n",
       "      <td>445.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>2227.337771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3596.0</td>\n",
       "      <td>2237.349571</td>\n",
       "      <td>691.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>3374.349571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35409</th>\n",
       "      <td>9557.0</td>\n",
       "      <td>2932.175128</td>\n",
       "      <td>768.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>4146.175128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35410</th>\n",
       "      <td>1864.0</td>\n",
       "      <td>2373.824959</td>\n",
       "      <td>324.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>3143.824959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35411</th>\n",
       "      <td>2674.0</td>\n",
       "      <td>2093.882146</td>\n",
       "      <td>791.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>3135.882146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35412</th>\n",
       "      <td>2234.0</td>\n",
       "      <td>2163.224374</td>\n",
       "      <td>687.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>3101.224374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35413</th>\n",
       "      <td>1441.0</td>\n",
       "      <td>1980.095307</td>\n",
       "      <td>452.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>2878.095307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35414 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual_total_delivery_duration  prep_duration_prediction  \\\n",
       "0                              2448.0               1929.168417   \n",
       "1                              1839.0               1682.649684   \n",
       "2                              1927.0               2376.807852   \n",
       "3                              2657.0               1531.337771   \n",
       "4                              3596.0               2237.349571   \n",
       "...                               ...                       ...   \n",
       "35409                          9557.0               2932.175128   \n",
       "35410                          1864.0               2373.824959   \n",
       "35411                          2674.0               2093.882146   \n",
       "35412                          2234.0               2163.224374   \n",
       "35413                          1441.0               1980.095307   \n",
       "\n",
       "       estimated_store_to_consumer_driving_duration  \\\n",
       "0                                             380.0   \n",
       "1                                             747.0   \n",
       "2                                             543.0   \n",
       "3                                             445.0   \n",
       "4                                             691.0   \n",
       "...                                             ...   \n",
       "35409                                         768.0   \n",
       "35410                                         324.0   \n",
       "35411                                         791.0   \n",
       "35412                                         687.0   \n",
       "35413                                         452.0   \n",
       "\n",
       "       estimated_order_place_duration  sum_total_delivery_direction  \n",
       "0                               251.0                   2560.168417  \n",
       "1                               446.0                   2875.649684  \n",
       "2                               251.0                   3170.807852  \n",
       "3                               251.0                   2227.337771  \n",
       "4                               446.0                   3374.349571  \n",
       "...                               ...                           ...  \n",
       "35409                           446.0                   4146.175128  \n",
       "35410                           446.0                   3143.824959  \n",
       "35411                           251.0                   3135.882146  \n",
       "35412                           251.0                   3101.224374  \n",
       "35413                           446.0                   2878.095307  \n",
       "\n",
       "[35414 rows x 5 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum the predictions up with non preparation activities such as order placing and driving.\n",
    "values_df['sum_total_delivery_direction'] = values_df['prep_duration_prediction'] + \\\n",
    "    values_df['estimated_store_to_consumer_driving_duration'] + values_df['estimated_order_place_duration']\n",
    "values_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2047.9078411118137"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check new error rate\n",
    "mean_squared_error(values_df['actual_total_delivery_duration'], values_df['sum_total_delivery_direction'], \\\n",
    "    squared=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The error is still high\n",
    "\n",
    "# Another approach\n",
    "What if we use another regression to obtain the actual total delivery duration instead of just summing up like above?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = values_df[['prep_duration_prediction', 'estimated_store_to_consumer_driving_duration', \\\n",
    "    'estimated_order_place_duration']]\n",
    "y = values_df['actual_total_delivery_duration']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE of:  LinearReg 986.9820328991567\n",
      "RMSE of:  Ridge 986.9820329035784\n",
      "RMSE of:  DecisionTree 1233.993037103546\n",
      "RMSE of:  RandomForest 1480.5985594993163\n",
      "RMSE of:  XGBoost 2490.2711674487477\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000435 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 519\n",
      "[LightGBM] [Info] Number of data points in the train set: 28331, number of used features: 3\n",
      "[LightGBM] [Info] Start training from score 2869.174261\n",
      "RMSE of:  LGBM 1037.3322000977241\n",
      "RMSE of:  MLP 988.9244884509578\n"
     ]
    }
   ],
   "source": [
    "regression_models = {\n",
    "    'LinearReg': linear_model.LinearRegression(),\n",
    "    'Ridge': linear_model.Ridge(),\n",
    "    'DecisionTree': tree.DecisionTreeRegressor(max_depth=6),\n",
    "    'RandomForest': RandomForestRegressor(),\n",
    "    'XGBoost': XGBRegressor(),\n",
    "    'LGBM': LGBMRegressor(),\n",
    "    'MLP': MLPRegressor(),\n",
    "}\n",
    "\n",
    "for model_name in regression_models.keys():\n",
    "    _, y_predict, _, _ = make_regression(\n",
    "        X_train, y_train, X_test, y_test, regression_models[model_name], model_name, verbose=False\n",
    "    )\n",
    "    print('RMSE of: ', model_name, mean_squared_error(y_test, y_predict, squared=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This approach has a better performance!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning\n",
    "### Rationale:\n",
    "Could Artificial Neural Networks have a better performance for the given problem? The first intuition is that MLP performance was not better than tree-based regression in the preparation duration prediction part. Hence, we doubt it. However, MLP has a good performance on the prediction of actual total delivery using preparation, driving, and order place duration. Let's give it a try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Neural Network\n",
    "def create_model(feature_set_size):\n",
    "\n",
    "    # define the model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(16, input_dim=feature_set_size, activation='relu'))\n",
    "    model.add(Dense(1, activation='linear'))\n",
    "\n",
    "    # compile the model\n",
    "    model.compile(optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----scaled with Standard Scaler----- included columns are selected_features_40\n",
      "\n",
      "feature_set_size: 3\n",
      "Epoch 1/100\n",
      "443/443 [==============================] - 0s 353us/step - loss: 1.1443 - root_mean_squared_error: 1.0697\n",
      "Epoch 2/100\n",
      "443/443 [==============================] - 0s 362us/step - loss: 1.1203 - root_mean_squared_error: 1.0584\n",
      "Epoch 3/100\n",
      "443/443 [==============================] - 0s 329us/step - loss: 1.1188 - root_mean_squared_error: 1.0577\n",
      "Epoch 4/100\n",
      "443/443 [==============================] - 0s 326us/step - loss: 1.1186 - root_mean_squared_error: 1.0576\n",
      "Epoch 5/100\n",
      "443/443 [==============================] - 0s 311us/step - loss: 1.1182 - root_mean_squared_error: 1.0574\n",
      "Epoch 6/100\n",
      "443/443 [==============================] - 0s 300us/step - loss: 1.1182 - root_mean_squared_error: 1.0575\n",
      "Epoch 7/100\n",
      "443/443 [==============================] - 0s 300us/step - loss: 1.1179 - root_mean_squared_error: 1.0573\n",
      "Epoch 8/100\n",
      "443/443 [==============================] - 0s 301us/step - loss: 1.1176 - root_mean_squared_error: 1.0572\n",
      "Epoch 9/100\n",
      "443/443 [==============================] - 0s 300us/step - loss: 1.1176 - root_mean_squared_error: 1.0571\n",
      "Epoch 10/100\n",
      "443/443 [==============================] - 0s 300us/step - loss: 1.1178 - root_mean_squared_error: 1.0573\n",
      "Epoch 11/100\n",
      "443/443 [==============================] - 0s 297us/step - loss: 1.1176 - root_mean_squared_error: 1.0572\n",
      "Epoch 12/100\n",
      "443/443 [==============================] - 0s 298us/step - loss: 1.1171 - root_mean_squared_error: 1.0569\n",
      "Epoch 13/100\n",
      "443/443 [==============================] - 0s 300us/step - loss: 1.1176 - root_mean_squared_error: 1.0572\n",
      "Epoch 14/100\n",
      "443/443 [==============================] - 0s 299us/step - loss: 1.1170 - root_mean_squared_error: 1.0569\n",
      "Epoch 15/100\n",
      "443/443 [==============================] - 0s 298us/step - loss: 1.1174 - root_mean_squared_error: 1.0571\n",
      "Epoch 16/100\n",
      "443/443 [==============================] - 0s 307us/step - loss: 1.1174 - root_mean_squared_error: 1.0571\n",
      "Epoch 17/100\n",
      "443/443 [==============================] - 0s 307us/step - loss: 1.1174 - root_mean_squared_error: 1.0571\n",
      "Epoch 18/100\n",
      "443/443 [==============================] - 0s 301us/step - loss: 1.1173 - root_mean_squared_error: 1.0570\n",
      "Epoch 19/100\n",
      "443/443 [==============================] - 0s 309us/step - loss: 1.1174 - root_mean_squared_error: 1.0571\n",
      "Epoch 20/100\n",
      "443/443 [==============================] - 0s 313us/step - loss: 1.1169 - root_mean_squared_error: 1.0568\n",
      "Epoch 21/100\n",
      "443/443 [==============================] - 0s 308us/step - loss: 1.1171 - root_mean_squared_error: 1.0569\n",
      "Epoch 22/100\n",
      "443/443 [==============================] - 0s 304us/step - loss: 1.1172 - root_mean_squared_error: 1.0570\n",
      "Epoch 23/100\n",
      "443/443 [==============================] - 0s 305us/step - loss: 1.1170 - root_mean_squared_error: 1.0569\n",
      "Epoch 24/100\n",
      "443/443 [==============================] - 0s 307us/step - loss: 1.1169 - root_mean_squared_error: 1.0568\n",
      "Epoch 25/100\n",
      "443/443 [==============================] - 0s 305us/step - loss: 1.1170 - root_mean_squared_error: 1.0569\n",
      "Epoch 26/100\n",
      "443/443 [==============================] - 0s 306us/step - loss: 1.1173 - root_mean_squared_error: 1.0570\n",
      "Epoch 27/100\n",
      "443/443 [==============================] - 0s 309us/step - loss: 1.1169 - root_mean_squared_error: 1.0568\n",
      "Epoch 28/100\n",
      "443/443 [==============================] - 0s 305us/step - loss: 1.1172 - root_mean_squared_error: 1.0570\n",
      "Epoch 29/100\n",
      "443/443 [==============================] - 0s 328us/step - loss: 1.1170 - root_mean_squared_error: 1.0569\n",
      "Epoch 30/100\n",
      "443/443 [==============================] - 0s 299us/step - loss: 1.1169 - root_mean_squared_error: 1.0568\n",
      "Epoch 31/100\n",
      "443/443 [==============================] - 0s 310us/step - loss: 1.1170 - root_mean_squared_error: 1.0569\n",
      "Epoch 32/100\n",
      "443/443 [==============================] - 0s 303us/step - loss: 1.1170 - root_mean_squared_error: 1.0569\n",
      "Epoch 33/100\n",
      "443/443 [==============================] - 0s 299us/step - loss: 1.1169 - root_mean_squared_error: 1.0569\n",
      "Epoch 34/100\n",
      "443/443 [==============================] - 0s 301us/step - loss: 1.1171 - root_mean_squared_error: 1.0569\n",
      "Epoch 35/100\n",
      "443/443 [==============================] - 0s 302us/step - loss: 1.1169 - root_mean_squared_error: 1.0569\n",
      "Epoch 36/100\n",
      "443/443 [==============================] - 0s 309us/step - loss: 1.1173 - root_mean_squared_error: 1.0570\n",
      "Epoch 37/100\n",
      "443/443 [==============================] - 0s 305us/step - loss: 1.1168 - root_mean_squared_error: 1.0568\n",
      "Epoch 38/100\n",
      "443/443 [==============================] - 0s 304us/step - loss: 1.1167 - root_mean_squared_error: 1.0567\n",
      "Epoch 39/100\n",
      "443/443 [==============================] - 0s 308us/step - loss: 1.1168 - root_mean_squared_error: 1.0568\n",
      "Epoch 40/100\n",
      "443/443 [==============================] - 0s 322us/step - loss: 1.1171 - root_mean_squared_error: 1.0570\n",
      "Epoch 41/100\n",
      "443/443 [==============================] - 0s 313us/step - loss: 1.1171 - root_mean_squared_error: 1.0569\n",
      "Epoch 42/100\n",
      "443/443 [==============================] - 0s 306us/step - loss: 1.1167 - root_mean_squared_error: 1.0567\n",
      "Epoch 43/100\n",
      "443/443 [==============================] - 0s 312us/step - loss: 1.1169 - root_mean_squared_error: 1.0568\n",
      "Epoch 44/100\n",
      "443/443 [==============================] - 0s 311us/step - loss: 1.1171 - root_mean_squared_error: 1.0569\n",
      "Epoch 45/100\n",
      "443/443 [==============================] - 0s 307us/step - loss: 1.1170 - root_mean_squared_error: 1.0569\n",
      "Epoch 46/100\n",
      "443/443 [==============================] - 0s 315us/step - loss: 1.1168 - root_mean_squared_error: 1.0568\n",
      "Epoch 47/100\n",
      "443/443 [==============================] - 0s 308us/step - loss: 1.1170 - root_mean_squared_error: 1.0569\n",
      "Epoch 48/100\n",
      "443/443 [==============================] - 0s 307us/step - loss: 1.1163 - root_mean_squared_error: 1.0566\n",
      "Epoch 49/100\n",
      "443/443 [==============================] - 0s 307us/step - loss: 1.1168 - root_mean_squared_error: 1.0568\n",
      "Epoch 50/100\n",
      "443/443 [==============================] - 0s 313us/step - loss: 1.1168 - root_mean_squared_error: 1.0568\n",
      "Epoch 51/100\n",
      "443/443 [==============================] - 0s 316us/step - loss: 1.1166 - root_mean_squared_error: 1.0567\n",
      "Epoch 52/100\n",
      "443/443 [==============================] - 0s 309us/step - loss: 1.1169 - root_mean_squared_error: 1.0568\n",
      "Epoch 53/100\n",
      "443/443 [==============================] - 0s 308us/step - loss: 1.1167 - root_mean_squared_error: 1.0567\n",
      "Epoch 54/100\n",
      "443/443 [==============================] - 0s 302us/step - loss: 1.1167 - root_mean_squared_error: 1.0568\n",
      "Epoch 55/100\n",
      "443/443 [==============================] - 0s 300us/step - loss: 1.1166 - root_mean_squared_error: 1.0567\n",
      "Epoch 56/100\n",
      "443/443 [==============================] - 0s 307us/step - loss: 1.1168 - root_mean_squared_error: 1.0568\n",
      "Epoch 57/100\n",
      "443/443 [==============================] - 0s 303us/step - loss: 1.1165 - root_mean_squared_error: 1.0567\n",
      "Epoch 58/100\n",
      "443/443 [==============================] - 0s 311us/step - loss: 1.1167 - root_mean_squared_error: 1.0567\n",
      "Epoch 59/100\n",
      "443/443 [==============================] - 0s 306us/step - loss: 1.1169 - root_mean_squared_error: 1.0568\n",
      "Epoch 60/100\n",
      "443/443 [==============================] - 0s 309us/step - loss: 1.1166 - root_mean_squared_error: 1.0567\n",
      "Epoch 61/100\n",
      "443/443 [==============================] - 0s 302us/step - loss: 1.1167 - root_mean_squared_error: 1.0567\n",
      "Epoch 62/100\n",
      "443/443 [==============================] - 0s 303us/step - loss: 1.1166 - root_mean_squared_error: 1.0567\n",
      "Epoch 63/100\n",
      "443/443 [==============================] - 0s 300us/step - loss: 1.1166 - root_mean_squared_error: 1.0567\n",
      "Epoch 64/100\n",
      "443/443 [==============================] - 0s 300us/step - loss: 1.1167 - root_mean_squared_error: 1.0568\n",
      "Epoch 65/100\n",
      "443/443 [==============================] - 0s 305us/step - loss: 1.1164 - root_mean_squared_error: 1.0566\n",
      "Epoch 66/100\n",
      "443/443 [==============================] - 0s 308us/step - loss: 1.1168 - root_mean_squared_error: 1.0568\n",
      "Epoch 67/100\n",
      "443/443 [==============================] - 0s 304us/step - loss: 1.1166 - root_mean_squared_error: 1.0567\n",
      "Epoch 68/100\n",
      "443/443 [==============================] - 0s 304us/step - loss: 1.1165 - root_mean_squared_error: 1.0566\n",
      "Epoch 69/100\n",
      "443/443 [==============================] - 0s 299us/step - loss: 1.1167 - root_mean_squared_error: 1.0568\n",
      "Epoch 70/100\n",
      "443/443 [==============================] - 0s 299us/step - loss: 1.1166 - root_mean_squared_error: 1.0567\n",
      "Epoch 71/100\n",
      "443/443 [==============================] - 0s 311us/step - loss: 1.1168 - root_mean_squared_error: 1.0568\n",
      "Epoch 72/100\n",
      "443/443 [==============================] - 0s 303us/step - loss: 1.1162 - root_mean_squared_error: 1.0565\n",
      "Epoch 73/100\n",
      "443/443 [==============================] - 0s 296us/step - loss: 1.1173 - root_mean_squared_error: 1.0570\n",
      "Epoch 74/100\n",
      "443/443 [==============================] - 0s 300us/step - loss: 1.1167 - root_mean_squared_error: 1.0567\n",
      "Epoch 75/100\n",
      "443/443 [==============================] - 0s 299us/step - loss: 1.1163 - root_mean_squared_error: 1.0566\n",
      "Epoch 76/100\n",
      "443/443 [==============================] - 0s 297us/step - loss: 1.1165 - root_mean_squared_error: 1.0567\n",
      "Epoch 77/100\n",
      "443/443 [==============================] - 0s 346us/step - loss: 1.1166 - root_mean_squared_error: 1.0567\n",
      "Epoch 78/100\n",
      "443/443 [==============================] - 0s 301us/step - loss: 1.1165 - root_mean_squared_error: 1.0566\n",
      "Epoch 79/100\n",
      "443/443 [==============================] - 0s 298us/step - loss: 1.1168 - root_mean_squared_error: 1.0568\n",
      "Epoch 80/100\n",
      "443/443 [==============================] - 0s 297us/step - loss: 1.1165 - root_mean_squared_error: 1.0567\n",
      "Epoch 81/100\n",
      "443/443 [==============================] - 0s 314us/step - loss: 1.1162 - root_mean_squared_error: 1.0565\n",
      "Epoch 82/100\n",
      "443/443 [==============================] - 0s 353us/step - loss: 1.1167 - root_mean_squared_error: 1.0568\n",
      "Epoch 83/100\n",
      "443/443 [==============================] - 0s 323us/step - loss: 1.1167 - root_mean_squared_error: 1.0567\n",
      "Epoch 84/100\n",
      "443/443 [==============================] - 0s 322us/step - loss: 1.1166 - root_mean_squared_error: 1.0567\n",
      "Epoch 85/100\n",
      "443/443 [==============================] - 0s 298us/step - loss: 1.1164 - root_mean_squared_error: 1.0566\n",
      "Epoch 86/100\n",
      "443/443 [==============================] - 0s 297us/step - loss: 1.1169 - root_mean_squared_error: 1.0569\n",
      "Epoch 87/100\n",
      "443/443 [==============================] - 0s 299us/step - loss: 1.1164 - root_mean_squared_error: 1.0566\n",
      "Epoch 88/100\n",
      "443/443 [==============================] - 0s 326us/step - loss: 1.1167 - root_mean_squared_error: 1.0567\n",
      "Epoch 89/100\n",
      "443/443 [==============================] - 0s 324us/step - loss: 1.1164 - root_mean_squared_error: 1.0566\n",
      "Epoch 90/100\n",
      "443/443 [==============================] - 0s 307us/step - loss: 1.1165 - root_mean_squared_error: 1.0566\n",
      "Epoch 91/100\n",
      "443/443 [==============================] - 0s 299us/step - loss: 1.1164 - root_mean_squared_error: 1.0566\n",
      "Epoch 92/100\n",
      "443/443 [==============================] - 0s 309us/step - loss: 1.1164 - root_mean_squared_error: 1.0566\n",
      "Epoch 93/100\n",
      "443/443 [==============================] - 0s 305us/step - loss: 1.1163 - root_mean_squared_error: 1.0566\n",
      "Epoch 94/100\n",
      "443/443 [==============================] - 0s 305us/step - loss: 1.1161 - root_mean_squared_error: 1.0565\n",
      "Epoch 95/100\n",
      "443/443 [==============================] - 0s 305us/step - loss: 1.1166 - root_mean_squared_error: 1.0567\n",
      "Epoch 96/100\n",
      "443/443 [==============================] - 0s 304us/step - loss: 1.1165 - root_mean_squared_error: 1.0567\n",
      "Epoch 97/100\n",
      "443/443 [==============================] - 0s 301us/step - loss: 1.1163 - root_mean_squared_error: 1.0566\n",
      "Epoch 98/100\n",
      "443/443 [==============================] - 0s 300us/step - loss: 1.1166 - root_mean_squared_error: 1.0567\n",
      "Epoch 99/100\n",
      "443/443 [==============================] - 0s 300us/step - loss: 1.1164 - root_mean_squared_error: 1.0566\n",
      "Epoch 100/100\n",
      "443/443 [==============================] - 0s 302us/step - loss: 1.1160 - root_mean_squared_error: 1.0564\n",
      "222/222 [==============================] - 0s 241us/step\n",
      "Error = 1002.4039001761578 in ANN\n"
     ]
    }
   ],
   "source": [
    "print(f\"-----scaled with {scaler_name}----- included columns are {feature_set_name}\")\n",
    "print(\"\")\n",
    "model_name = 'ANN'\n",
    "scaler_name = 'Standard Scaler'\n",
    "X = values_df[['prep_duration_prediction', 'estimated_store_to_consumer_driving_duration', \\\n",
    "    'estimated_order_place_duration']]\n",
    "y = values_df['actual_total_delivery_duration']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_scaled, y_scaled, X_scaler, y_scaler = scale(scalers[scaler_name], X, y)\n",
    "X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled = train_test_split(\n",
    "    X_scaled, y_scaled, test_size=0.2, random_state=42\n",
    ")\n",
    "print('feature_set_size:', X_train_scaled.shape[1])\n",
    "model = create_model(feature_set_size = X_train_scaled.shape[1])\n",
    "history = model.fit(X_train_scaled, y_train_scaled, epochs = 100, batch_size=64, verbose=1)\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "rmse_error = rmse_with_inv_transform(y_scaler, y_test, y_pred, model_name)\n",
    "\n",
    "pred_dict['regression_model'].append(model_name)\n",
    "pred_dict['feature_set'].append(feature_set_name)\n",
    "pred_dict['scaler_name'].append(scaler_name)\n",
    "pred_dict['RMSE'].append(rmse_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.8 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}