workbook_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(450000, 20)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>order_time</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>allot_time</th>\n",
       "      <th>accept_time</th>\n",
       "      <th>pickup_time</th>\n",
       "      <th>delivered_time</th>\n",
       "      <th>rider_id</th>\n",
       "      <th>first_mile_distance</th>\n",
       "      <th>last_mile_distance</th>\n",
       "      <th>alloted_orders</th>\n",
       "      <th>delivered_orders</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>undelivered_orders</th>\n",
       "      <th>lifetime_order_count</th>\n",
       "      <th>reassignment_method</th>\n",
       "      <th>reassignment_reason</th>\n",
       "      <th>reassigned_order</th>\n",
       "      <th>session_time</th>\n",
       "      <th>cancelled_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-26 02:21:35</td>\n",
       "      <td>556753</td>\n",
       "      <td>2021-01-26 00:00:00</td>\n",
       "      <td>2021-01-26 02:21:59</td>\n",
       "      <td>2021-01-26 02:22:08</td>\n",
       "      <td>2021-01-26 02:32:51</td>\n",
       "      <td>2021-01-26 02:49:47</td>\n",
       "      <td>11696</td>\n",
       "      <td>1.5666</td>\n",
       "      <td>2.65</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-26 02:33:16</td>\n",
       "      <td>556754</td>\n",
       "      <td>2021-01-26 00:00:00</td>\n",
       "      <td>2021-01-26 02:33:57</td>\n",
       "      <td>2021-01-26 02:34:45</td>\n",
       "      <td>2021-01-26 02:50:25</td>\n",
       "      <td>2021-01-26 03:11:15</td>\n",
       "      <td>18117</td>\n",
       "      <td>2.5207</td>\n",
       "      <td>2.76</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.266667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-26 02:39:49</td>\n",
       "      <td>556755</td>\n",
       "      <td>2021-01-26 00:00:00</td>\n",
       "      <td>2021-01-26 02:39:57</td>\n",
       "      <td>2021-01-26 02:40:13</td>\n",
       "      <td>2021-01-26 02:56:00</td>\n",
       "      <td>2021-01-26 03:12:46</td>\n",
       "      <td>18623</td>\n",
       "      <td>2.2074</td>\n",
       "      <td>4.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.816667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-26 02:47:53</td>\n",
       "      <td>556756</td>\n",
       "      <td>2021-01-26 00:00:00</td>\n",
       "      <td>2021-01-26 02:48:25</td>\n",
       "      <td>2021-01-26 02:49:06</td>\n",
       "      <td>2021-01-26 03:21:51</td>\n",
       "      <td>2021-01-26 03:41:05</td>\n",
       "      <td>15945</td>\n",
       "      <td>2.1894</td>\n",
       "      <td>6.38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.533333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-26 03:06:30</td>\n",
       "      <td>556757</td>\n",
       "      <td>2021-01-26 00:00:00</td>\n",
       "      <td>2021-01-26 03:07:21</td>\n",
       "      <td>2021-01-26 03:07:57</td>\n",
       "      <td>2021-01-26 03:31:38</td>\n",
       "      <td>2021-01-26 04:00:15</td>\n",
       "      <td>17589</td>\n",
       "      <td>2.7870</td>\n",
       "      <td>4.01</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            order_time  order_id           order_date           allot_time  \\\n",
       "0  2021-01-26 02:21:35    556753  2021-01-26 00:00:00  2021-01-26 02:21:59   \n",
       "1  2021-01-26 02:33:16    556754  2021-01-26 00:00:00  2021-01-26 02:33:57   \n",
       "2  2021-01-26 02:39:49    556755  2021-01-26 00:00:00  2021-01-26 02:39:57   \n",
       "3  2021-01-26 02:47:53    556756  2021-01-26 00:00:00  2021-01-26 02:48:25   \n",
       "4  2021-01-26 03:06:30    556757  2021-01-26 00:00:00  2021-01-26 03:07:21   \n",
       "\n",
       "           accept_time          pickup_time       delivered_time  rider_id  \\\n",
       "0  2021-01-26 02:22:08  2021-01-26 02:32:51  2021-01-26 02:49:47     11696   \n",
       "1  2021-01-26 02:34:45  2021-01-26 02:50:25  2021-01-26 03:11:15     18117   \n",
       "2  2021-01-26 02:40:13  2021-01-26 02:56:00  2021-01-26 03:12:46     18623   \n",
       "3  2021-01-26 02:49:06  2021-01-26 03:21:51  2021-01-26 03:41:05     15945   \n",
       "4  2021-01-26 03:07:57  2021-01-26 03:31:38  2021-01-26 04:00:15     17589   \n",
       "\n",
       "   first_mile_distance  last_mile_distance  alloted_orders  delivered_orders  \\\n",
       "0               1.5666                2.65            46.0              46.0   \n",
       "1               2.5207                2.76             8.0               8.0   \n",
       "2               2.2074                4.80             1.0               1.0   \n",
       "3               2.1894                6.38             1.0               1.0   \n",
       "4               2.7870                4.01            34.0              34.0   \n",
       "\n",
       "   cancelled  undelivered_orders  lifetime_order_count reassignment_method  \\\n",
       "0          0                 0.0                 621.0                 NaN   \n",
       "1          0                 0.0                 105.0                 NaN   \n",
       "2          0                 0.0                  66.0                 NaN   \n",
       "3          0                 0.0                 127.0                 NaN   \n",
       "4          0                 0.0                  84.0                 NaN   \n",
       "\n",
       "  reassignment_reason  reassigned_order  session_time cancelled_time  \n",
       "0                 NaN               NaN           NaN            NaN  \n",
       "1                 NaN               NaN      3.266667            NaN  \n",
       "2                 NaN               NaN      9.816667            NaN  \n",
       "3                 NaN               NaN     17.533333            NaN  \n",
       "4                 NaN               NaN      1.350000            NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_time                   0\n",
       "order_id                     0\n",
       "order_date                   0\n",
       "allot_time                   0\n",
       "accept_time                157\n",
       "pickup_time               2421\n",
       "delivered_time            5218\n",
       "rider_id                     0\n",
       "first_mile_distance          0\n",
       "last_mile_distance           0\n",
       "alloted_orders           16948\n",
       "delivered_orders         17341\n",
       "cancelled                    0\n",
       "undelivered_orders       17341\n",
       "lifetime_order_count        53\n",
       "reassignment_method     436256\n",
       "reassignment_reason     436247\n",
       "reassigned_order        436247\n",
       "session_time              3675\n",
       "cancelled_time          444782\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping the 4 columns with maximum missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['reassignment_method','reassignment_reason','reassigned_order','cancelled_time'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['order_time','allot_time','accept_time','pickup_time','delivered_time'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(450000, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17341"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['delivered_orders'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17341"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['undelivered_orders'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delivered,undelivered orders contain null values to be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                    0\n",
       "order_date                  0\n",
       "rider_id                    0\n",
       "first_mile_distance         0\n",
       "last_mile_distance          0\n",
       "alloted_orders          16948\n",
       "delivered_orders        17341\n",
       "cancelled                   0\n",
       "undelivered_orders      17341\n",
       "lifetime_order_count       53\n",
       "session_time             3675\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new=df.dropna(axis=0) # dropping null values in all the rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                0\n",
       "order_date              0\n",
       "rider_id                0\n",
       "first_mile_distance     0\n",
       "last_mile_distance      0\n",
       "alloted_orders          0\n",
       "delivered_orders        0\n",
       "cancelled               0\n",
       "undelivered_orders      0\n",
       "lifetime_order_count    0\n",
       "session_time            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No more missing values in dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(429315, 11)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>rider_id</th>\n",
       "      <th>first_mile_distance</th>\n",
       "      <th>last_mile_distance</th>\n",
       "      <th>alloted_orders</th>\n",
       "      <th>delivered_orders</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>undelivered_orders</th>\n",
       "      <th>lifetime_order_count</th>\n",
       "      <th>session_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>556754</td>\n",
       "      <td>2021-01-26 00:00:00</td>\n",
       "      <td>18117</td>\n",
       "      <td>2.520700</td>\n",
       "      <td>2.76</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>3.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>556755</td>\n",
       "      <td>2021-01-26 00:00:00</td>\n",
       "      <td>18623</td>\n",
       "      <td>2.207400</td>\n",
       "      <td>4.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>556756</td>\n",
       "      <td>2021-01-26 00:00:00</td>\n",
       "      <td>15945</td>\n",
       "      <td>2.189400</td>\n",
       "      <td>6.38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>17.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>556757</td>\n",
       "      <td>2021-01-26 00:00:00</td>\n",
       "      <td>17589</td>\n",
       "      <td>2.787000</td>\n",
       "      <td>4.01</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>556760</td>\n",
       "      <td>2021-01-26 00:00:00</td>\n",
       "      <td>8493</td>\n",
       "      <td>0.025681</td>\n",
       "      <td>0.16</td>\n",
       "      <td>54.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>44.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id           order_date  rider_id  first_mile_distance  \\\n",
       "1    556754  2021-01-26 00:00:00     18117             2.520700   \n",
       "2    556755  2021-01-26 00:00:00     18623             2.207400   \n",
       "3    556756  2021-01-26 00:00:00     15945             2.189400   \n",
       "4    556757  2021-01-26 00:00:00     17589             2.787000   \n",
       "7    556760  2021-01-26 00:00:00      8493             0.025681   \n",
       "\n",
       "   last_mile_distance  alloted_orders  delivered_orders  cancelled  \\\n",
       "1                2.76             8.0               8.0          0   \n",
       "2                4.80             1.0               1.0          0   \n",
       "3                6.38             1.0               1.0          0   \n",
       "4                4.01            34.0              34.0          0   \n",
       "7                0.16            54.0              53.0          0   \n",
       "\n",
       "   undelivered_orders  lifetime_order_count  session_time  \n",
       "1                 0.0                 105.0      3.266667  \n",
       "2                 0.0                  66.0      9.816667  \n",
       "3                 0.0                 127.0     17.533333  \n",
       "4                 0.0                  84.0      1.350000  \n",
       "7                 1.0                 270.0     44.166667  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize=(25,10))\n",
    "sns.heatmap(df_new.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_new.drop(columns=['order_date','cancelled'],axis=1)\n",
    "y=df_new['cancelled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "clf = tree.DecisionTreeClassifier(max_depth=2)\n",
    "clf = clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "k=clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.01401069145034"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(k,y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(steps=[ ('dec_tree',clf)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_components = list(range(1,X.shape[1]+1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=Pipeline(steps=[('dec_tree',\n",
       "                                        DecisionTreeClassifier(max_depth=7))]),\n",
       "             param_grid={'dec_tree__criterion': ['gini', 'entropy'],\n",
       "                         'dec_tree__max_depth': [2, 4, 6, 8, 10, 12]})"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "criterion = ['gini', 'entropy']\n",
    "max_depth = [2,4,6,8,10,12]\n",
    "parameters = dict(dec_tree__criterion=criterion,dec_tree__max_depth=max_depth)   \n",
    "clf_GS = GridSearchCV(pipe, parameters)\n",
    "clf_GS.fit(X, y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Criterion: gini\n",
      "Best max_depth: 2\n",
      "\n",
      "DecisionTreeClassifier(max_depth=2)\n"
     ]
    }
   ],
   "source": [
    "print('Best Criterion:', clf_GS.best_estimator_.get_params()['dec_tree__criterion'])\n",
    "print('Best max_depth:', clf_GS.best_estimator_.get_params()['dec_tree__max_depth'])\n",
    "print(); \n",
    "print(clf_GS.best_estimator_.get_params()['dec_tree'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=2, random_state=0)"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = make_classification(n_samples=1000, n_features=9,\n",
    "                          n_informative=2, n_redundant=0,\n",
    "                     random_state=0, shuffle=False)\n",
    "clf = RandomForestClassifier(max_depth=2, random_state=0)\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from sklearn import svm\n",
    "#clf = svm.SVC()\n",
    "#clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 30 candidates, totalling 300 fits\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier()\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "k_range = list(range(1, 31))\n",
    "param_grid = dict(n_neighbors=k_range)\n",
    "  \n",
    "# defining parameter range\n",
    "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy', return_train_score=False,verbose=1)\n",
    "  \n",
    "# fitting the model for grid search\n",
    "grid_search=grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23:56:32] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
    "              gamma=0, gpu_id=-1, importance_type=None,\n",
    "              interaction_constraints='', learning_rate=0.300000012,\n",
    "              max_delta_step=0, max_depth=6, min_child_weight=1,\n",
    "              monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
    "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
    "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
    "              tree_method='exact', validate_parameters=1, verbosity=None)\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_new.drop(columns=['order_date','cancelled'],axis=1)\n",
    "y=df_new['cancelled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyper Parameter Optimization\n",
    "\n",
    "params={\n",
    " \"learning_rate\"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,\n",
    " \"max_depth\"        : [ 3, 4, 5, 6, 8, 10, 12, 15],\n",
    " \"min_child_weight\" : [ 1, 3, 5, 7 ],\n",
    " \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],\n",
    " \"colsample_bytree\" : [ 0.3, 0.4, 0.5 , 0.7 ]\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyperparameter optimization using RandomizedSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "import xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=xgboost.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search=RandomizedSearchCV(classifier,param_distributions=params,n_iter=5,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:23:00] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:23:00] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:23:01] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:23:01] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.554 total time= 2.9min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.588 total time= 2.9min\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.558 total time= 2.9min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.541 total time= 2.9min\n",
      "[15:25:53] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:25:55] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:25:55] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:25:56] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.579 total time= 2.8min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:28:42] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.563 total time= 4.8min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.587 total time= 4.8min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.556 total time= 4.9min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:30:45] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:30:45] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:30:47] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.598 total time= 1.5min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.568 total time= 1.5min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:32:14] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:32:15] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.525 total time= 5.0min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:33:44] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.528 total time= 1.6min\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.536 total time= 1.5min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:33:48] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:33:48] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.622 total time= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.619 total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:35:02] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:35:03] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.543 total time= 1.4min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:35:07] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.572 total time= 4.7min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:35:27] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.636 total time=  51.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.609 total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.611 total time= 1.3min\n",
      "[15:36:18] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:36:18] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:36:19] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.614 total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:36:23] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.669 total time=  47.5s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.649 total time=  48.3s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.642 total time=  47.5s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.620 total time=  45.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:37:07] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           enable_categorical=False, gamma=None,\n",
       "                                           gpu_id=None, importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None,\n",
       "                                           max_delta_step=None, max_depth=None,\n",
       "                                           min_child_weight=None, missing=nan,\n",
       "                                           monotone_constraints...\n",
       "                                           reg_alpha=None, reg_lambda=None,\n",
       "                                           scale_pos_weight=None,\n",
       "                                           subsample=None, tree_method=None,\n",
       "                                           validate_parameters=None,\n",
       "                                           verbosity=None),\n",
       "                   n_iter=5, n_jobs=-1,\n",
       "                   param_distributions={'colsample_bytree': [0.3, 0.4, 0.5,\n",
       "                                                             0.7],\n",
       "                                        'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                                        'learning_rate': [0.05, 0.1, 0.15, 0.2,\n",
       "                                                          0.25, 0.3],\n",
       "                                        'max_depth': [3, 4, 5, 6, 8, 10, 12,\n",
       "                                                      15],\n",
       "                                        'min_child_weight': [1, 3, 5, 7]},\n",
       "                   scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.4,\n",
       "              enable_categorical=False, gamma=0.1, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.05, max_delta_step=0, max_depth=4,\n",
       "              min_child_weight=3, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, num_parallel_tree=1, predictor='auto',\n",
       "              random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "              subsample=1, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=None)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=xgboost.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=0.4,\n",
    "              enable_categorical=False, gamma=0.1, gpu_id=-1,\n",
    "              importance_type=None, interaction_constraints='',\n",
    "              learning_rate=0.05, max_delta_step=0, max_depth=4,\n",
    "              min_child_weight=3, missing=1, monotone_constraints='()',\n",
    "              n_estimators=100, n_jobs=4, num_parallel_tree=1, predictor='auto',\n",
    "              random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
    "              subsample=1, tree_method='exact', validate_parameters=1,\n",
    "              verbosity=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
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       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>65.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449999</th>\n",
       "      <td>130230</td>\n",
       "      <td>2078</td>\n",
       "      <td>1.892500</td>\n",
       "      <td>0.09</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>212.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>429315 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        order_id  rider_id  first_mile_distance  last_mile_distance  \\\n",
       "1         556754     18117             2.520700                2.76   \n",
       "2         556755     18623             2.207400                4.80   \n",
       "3         556756     15945             2.189400                6.38   \n",
       "4         556757     17589             2.787000                4.01   \n",
       "7         556760      8493             0.025681                0.16   \n",
       "...          ...       ...                  ...                 ...   \n",
       "449995    130226      1006             0.578900                0.19   \n",
       "449996    130227       279             1.986300                1.19   \n",
       "449997    130228      3161             1.594400                1.61   \n",
       "449998    130229      9396             2.893900                4.68   \n",
       "449999    130230      2078             1.892500                0.09   \n",
       "\n",
       "        alloted_orders  delivered_orders  undelivered_orders  \\\n",
       "1                  8.0               8.0                 0.0   \n",
       "2                  1.0               1.0                 0.0   \n",
       "3                  1.0               1.0                 0.0   \n",
       "4                 34.0              34.0                 0.0   \n",
       "7                 54.0              53.0                 1.0   \n",
       "...                ...               ...                 ...   \n",
       "449995             4.0               4.0                 0.0   \n",
       "449996            81.0              81.0                 0.0   \n",
       "449997            28.0              28.0                 0.0   \n",
       "449998            72.0              72.0                 0.0   \n",
       "449999            30.0              30.0                 0.0   \n",
       "\n",
       "        lifetime_order_count  session_time  \n",
       "1                      105.0      3.266667  \n",
       "2                       66.0      9.816667  \n",
       "3                      127.0     17.533333  \n",
       "4                       84.0      1.350000  \n",
       "7                      270.0     44.166667  \n",
       "...                      ...           ...  \n",
       "449995                 127.0    369.516667  \n",
       "449996                 105.0    239.133333  \n",
       "449997                1488.0    204.150000  \n",
       "449998                 105.0     65.583333  \n",
       "449999                 108.0    212.000000  \n",
       "\n",
       "[429315 rows x 9 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:46:02] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.4,\n",
       "              enable_categorical=False, gamma=0.1, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.05, max_delta_step=0, max_depth=4,\n",
       "              min_child_weight=3, missing=1, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, num_parallel_tree=1, predictor='auto',\n",
       "              random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "              subsample=1, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=None)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:49:51] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:50:15] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:50:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:51:09] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abhaylal/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:51:36] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(classifier,X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99011216, 0.99007722, 0.99008886, 0.99008886, 0.99013545])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decission tree classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_new.drop(columns=['order_date','cancelled'],axis=1)\n",
    "y=df_new['cancelled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9920338213200098"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(min_samples_split=50, random_state=0)\n",
    "\n",
    "# 2. Fit\n",
    "dtc.fit(X,y)\n",
    "\n",
    "# 3. Predict, there're 4 features in the iris dataset\n",
    "y_pred_class = dtc.predict(X)\n",
    "\n",
    "# Accuracy\n",
    "metrics.accuracy_score(y, y_pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=DecisionTreeClassifier(min_samples_split=50,\n",
       "                                              random_state=0),\n",
       "             param_grid={'min_samples_split': [49, 50, 51, 52, 53, 54, 55, 56,\n",
       "                                               57, 58, 59]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "# Define the parameter values that should be searched\n",
    "sample_split_range = list(range(49, 60))\n",
    "\n",
    "# Create a parameter grid: map the parameter names to the values that should be searched\n",
    "# Simply a python dictionary\n",
    "# Key: parameter name\n",
    "# Value: list of values that should be searched for that parameter\n",
    "# Single key-value pair for param_grid\n",
    "param_grid = dict(min_samples_split=sample_split_range)\n",
    "\n",
    "# instantiate the grid\n",
    "grid = GridSearchCV(dtc, param_grid, cv=10, scoring='roc_auc')\n",
    "\n",
    "# fit the grid with data\n",
    "grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5415216233781113\n",
      "{'min_samples_split': 55}\n",
      "DecisionTreeClassifier(min_samples_split=55, random_state=0)\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "\n",
    "# Single best score achieved across all params (min_samples_split)\n",
    "print(grid.best_score_)\n",
    "\n",
    "# Dictionary containing the parameters (min_samples_split) used to generate that score\n",
    "print(grid.best_params_)\n",
    "\n",
    "# Actual model object fit with those best parameters\n",
    "# Shows default parameters that we did not specify\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc_new=DecisionTreeClassifier(min_samples_split=55, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(min_samples_split=55, random_state=0)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc_new.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>order_id</th>\n",
       "      <th>rider_id</th>\n",
       "      <th>first_mile_distance</th>\n",
       "      <th>last_mile_distance</th>\n",
       "      <th>alloted_orders</th>\n",
       "      <th>delivered_orders</th>\n",
       "      <th>undelivered_orders</th>\n",
       "      <th>lifetime_order_count</th>\n",
       "      <th>session_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>556754</td>\n",
       "      <td>18117</td>\n",
       "      <td>2.520700</td>\n",
       "      <td>2.76</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>3.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>556755</td>\n",
       "      <td>18623</td>\n",
       "      <td>2.207400</td>\n",
       "      <td>4.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>556756</td>\n",
       "      <td>15945</td>\n",
       "      <td>2.189400</td>\n",
       "      <td>6.38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>17.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>556757</td>\n",
       "      <td>17589</td>\n",
       "      <td>2.787000</td>\n",
       "      <td>4.01</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>556760</td>\n",
       "      <td>8493</td>\n",
       "      <td>0.025681</td>\n",
       "      <td>0.16</td>\n",
       "      <td>54.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>44.166667</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449995</th>\n",
       "      <td>130226</td>\n",
       "      <td>1006</td>\n",
       "      <td>0.578900</td>\n",
       "      <td>0.19</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>369.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449996</th>\n",
       "      <td>130227</td>\n",
       "      <td>279</td>\n",
       "      <td>1.986300</td>\n",
       "      <td>1.19</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>239.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449997</th>\n",
       "      <td>130228</td>\n",
       "      <td>3161</td>\n",
       "      <td>1.594400</td>\n",
       "      <td>1.61</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1488.0</td>\n",
       "      <td>204.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449998</th>\n",
       "      <td>130229</td>\n",
       "      <td>9396</td>\n",
       "      <td>2.893900</td>\n",
       "      <td>4.68</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>65.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449999</th>\n",
       "      <td>130230</td>\n",
       "      <td>2078</td>\n",
       "      <td>1.892500</td>\n",
       "      <td>0.09</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>212.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>429315 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        order_id  rider_id  first_mile_distance  last_mile_distance  \\\n",
       "1         556754     18117             2.520700                2.76   \n",
       "2         556755     18623             2.207400                4.80   \n",
       "3         556756     15945             2.189400                6.38   \n",
       "4         556757     17589             2.787000                4.01   \n",
       "7         556760      8493             0.025681                0.16   \n",
       "...          ...       ...                  ...                 ...   \n",
       "449995    130226      1006             0.578900                0.19   \n",
       "449996    130227       279             1.986300                1.19   \n",
       "449997    130228      3161             1.594400                1.61   \n",
       "449998    130229      9396             2.893900                4.68   \n",
       "449999    130230      2078             1.892500                0.09   \n",
       "\n",
       "        alloted_orders  delivered_orders  undelivered_orders  \\\n",
       "1                  8.0               8.0                 0.0   \n",
       "2                  1.0               1.0                 0.0   \n",
       "3                  1.0               1.0                 0.0   \n",
       "4                 34.0              34.0                 0.0   \n",
       "7                 54.0              53.0                 1.0   \n",
       "...                ...               ...                 ...   \n",
       "449995             4.0               4.0                 0.0   \n",
       "449996            81.0              81.0                 0.0   \n",
       "449997            28.0              28.0                 0.0   \n",
       "449998            72.0              72.0                 0.0   \n",
       "449999            30.0              30.0                 0.0   \n",
       "\n",
       "        lifetime_order_count  session_time  \n",
       "1                      105.0      3.266667  \n",
       "2                       66.0      9.816667  \n",
       "3                      127.0     17.533333  \n",
       "4                       84.0      1.350000  \n",
       "7                      270.0     44.166667  \n",
       "...                      ...           ...  \n",
       "449995                 127.0    369.516667  \n",
       "449996                 105.0    239.133333  \n",
       "449997                1488.0    204.150000  \n",
       "449998                 105.0     65.583333  \n",
       "449999                 108.0    212.000000  \n",
       "\n",
       "[429315 rows x 9 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_time</th>\n",
       "      <th>order_id</th>\n",
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       "      <th>0</th>\n",
       "      <td>2021-02-06 10:03:24</td>\n",
       "      <td>130231</td>\n",
       "      <td>2021-02-06 00:00:00</td>\n",
       "      <td>2021-02-06 10:03:49</td>\n",
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       "      <td>273.400000</td>\n",
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       "      <th>1</th>\n",
       "      <td>2021-02-06 10:03:26</td>\n",
       "      <td>130232</td>\n",
       "      <td>2021-02-06 00:00:00</td>\n",
       "      <td>2021-02-06 10:03:27</td>\n",
       "      <td>2021-02-06 10:03:36</td>\n",
       "      <td>3541</td>\n",
       "      <td>2.070900</td>\n",
       "      <td>5.84</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>252.100000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-02-06 10:03:27</td>\n",
       "      <td>130233</td>\n",
       "      <td>2021-02-06 00:00:00</td>\n",
       "      <td>2021-02-06 10:04:14</td>\n",
       "      <td>2021-02-06 10:05:34</td>\n",
       "      <td>603</td>\n",
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       "      <td>289.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2214.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241.383333</td>\n",
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       "      <th>3</th>\n",
       "      <td>2021-02-06 10:03:29</td>\n",
       "      <td>130234</td>\n",
       "      <td>2021-02-06 00:00:00</td>\n",
       "      <td>2021-02-06 10:03:30</td>\n",
       "      <td>2021-02-06 10:03:53</td>\n",
       "      <td>3414</td>\n",
       "      <td>1.903900</td>\n",
       "      <td>2.59</td>\n",
       "      <td>125.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1020.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>2021-02-06 10:03:35</td>\n",
       "      <td>130235</td>\n",
       "      <td>2021-02-06 00:00:00</td>\n",
       "      <td>2021-02-06 10:03:43</td>\n",
       "      <td>2021-02-06 10:04:43</td>\n",
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       "      <td>0.94</td>\n",
       "      <td>352.0</td>\n",
       "      <td>350.0</td>\n",
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       "      <td>7284.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>144839</th>\n",
       "      <td>2021-02-09 20:13:39</td>\n",
       "      <td>41184</td>\n",
       "      <td>2021-02-09 00:00:00</td>\n",
       "      <td>2021-02-09 20:13:40</td>\n",
       "      <td>2021-02-09 20:13:49</td>\n",
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       "      <td>413.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>144840</th>\n",
       "      <td>2021-02-09 20:21:36</td>\n",
       "      <td>41185</td>\n",
       "      <td>2021-02-09 00:00:00</td>\n",
       "      <td>2021-02-09 20:21:59</td>\n",
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       "      <td>2021-02-09 00:00:00</td>\n",
       "      <td>2021-02-09 20:25:03</td>\n",
       "      <td>2021-02-09 20:26:10</td>\n",
       "      <td>5040</td>\n",
       "      <td>2.049400</td>\n",
       "      <td>1.26</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
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       "      <td>119.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259.000000</td>\n",
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       "    <tr>\n",
       "      <th>144842</th>\n",
       "      <td>2021-02-09 20:28:07</td>\n",
       "      <td>41187</td>\n",
       "      <td>2021-02-09 00:00:00</td>\n",
       "      <td>2021-02-09 20:28:29</td>\n",
       "      <td>2021-02-09 20:28:53</td>\n",
       "      <td>2946</td>\n",
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       "      <td>231.0</td>\n",
       "      <td>228.0</td>\n",
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       "      <td>2021-02-09 20:28:24</td>\n",
       "      <td>41188</td>\n",
       "      <td>2021-02-09 00:00:00</td>\n",
       "      <td>2021-02-09 20:28:29</td>\n",
       "      <td>2021-02-09 20:29:47</td>\n",
       "      <td>8488</td>\n",
       "      <td>1.702100</td>\n",
       "      <td>4.13</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.533333</td>\n",
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       "<p>144844 rows × 16 columns</p>\n",
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      ],
      "text/plain": [
       "                 order_time  order_id           order_date  \\\n",
       "0       2021-02-06 10:03:24    130231  2021-02-06 00:00:00   \n",
       "1       2021-02-06 10:03:26    130232  2021-02-06 00:00:00   \n",
       "2       2021-02-06 10:03:27    130233  2021-02-06 00:00:00   \n",
       "3       2021-02-06 10:03:29    130234  2021-02-06 00:00:00   \n",
       "4       2021-02-06 10:03:35    130235  2021-02-06 00:00:00   \n",
       "...                     ...       ...                  ...   \n",
       "144839  2021-02-09 20:13:39     41184  2021-02-09 00:00:00   \n",
       "144840  2021-02-09 20:21:36     41185  2021-02-09 00:00:00   \n",
       "144841  2021-02-09 20:24:25     41186  2021-02-09 00:00:00   \n",
       "144842  2021-02-09 20:28:07     41187  2021-02-09 00:00:00   \n",
       "144843  2021-02-09 20:28:24     41188  2021-02-09 00:00:00   \n",
       "\n",
       "                 allot_time          accept_time  rider_id  \\\n",
       "0       2021-02-06 10:03:49  2021-02-06 10:04:15     12884   \n",
       "1       2021-02-06 10:03:27  2021-02-06 10:03:36      3541   \n",
       "2       2021-02-06 10:04:14  2021-02-06 10:05:34       603   \n",
       "3       2021-02-06 10:03:30  2021-02-06 10:03:53      3414   \n",
       "4       2021-02-06 10:03:43  2021-02-06 10:04:43      1426   \n",
       "...                     ...                  ...       ...   \n",
       "144839  2021-02-09 20:13:40  2021-02-09 20:13:49      7141   \n",
       "144840  2021-02-09 20:21:59  2021-02-09 20:22:33      8113   \n",
       "144841  2021-02-09 20:25:03  2021-02-09 20:26:10      5040   \n",
       "144842  2021-02-09 20:28:29  2021-02-09 20:28:53      2946   \n",
       "144843  2021-02-09 20:28:29  2021-02-09 20:29:47      8488   \n",
       "\n",
       "        first_mile_distance  last_mile_distance  alloted_orders  \\\n",
       "0                  1.658500                4.54           216.0   \n",
       "1                  2.070900                5.84            52.0   \n",
       "2                  1.388400                0.99           289.0   \n",
       "3                  1.903900                2.59           125.0   \n",
       "4                  0.827500                0.94           352.0   \n",
       "...                     ...                 ...             ...   \n",
       "144839             1.417000                3.96            52.0   \n",
       "144840             0.775600                1.61            95.0   \n",
       "144841             2.049400                1.26            77.0   \n",
       "144842             0.080494                5.50           231.0   \n",
       "144843             1.702100                4.13            28.0   \n",
       "\n",
       "        delivered_orders  undelivered_orders  lifetime_order_count  \\\n",
       "0                  215.0                 1.0                 747.0   \n",
       "1                   52.0                 0.0                  75.0   \n",
       "2                  289.0                 0.0                2214.0   \n",
       "3                  122.0                 3.0                1020.0   \n",
       "4                  350.0                 2.0                7284.0   \n",
       "...                  ...                 ...                   ...   \n",
       "144839              52.0                 0.0                 413.0   \n",
       "144840              93.0                 2.0                 284.0   \n",
       "144841              77.0                 0.0                 119.0   \n",
       "144842             228.0                 3.0                1759.0   \n",
       "144843              28.0                 0.0                  43.0   \n",
       "\n",
       "       reassignment_method reassignment_reason  reassigned_order  session_time  \n",
       "0                      NaN                 NaN               NaN    273.400000  \n",
       "1                      NaN                 NaN               NaN    252.100000  \n",
       "2                      NaN                 NaN               NaN    241.383333  \n",
       "3                      NaN                 NaN               NaN    291.933333  \n",
       "4                      NaN                 NaN               NaN    247.133333  \n",
       "...                    ...                 ...               ...           ...  \n",
       "144839                 NaN                 NaN               NaN    179.266667  \n",
       "144840                 NaN                 NaN               NaN    244.400000  \n",
       "144841                 NaN                 NaN               NaN    259.000000  \n",
       "144842                 NaN                 NaN               NaN    814.283333  \n",
       "144843                 NaN                 NaN               NaN    161.533333  \n",
       "\n",
       "[144844 rows x 16 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(columns=['reassignment_method','reassignment_reason','reassigned_order'],axis=1,inplace=True)\n",
    "test.drop(columns=['order_time','allot_time','accept_time'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test=test.drop(columns=['order_date'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>125.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1020.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>130235</td>\n",
       "      <td>1426</td>\n",
       "      <td>0.827500</td>\n",
       "      <td>0.94</td>\n",
       "      <td>352.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7284.0</td>\n",
       "      <td>247.133333</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144839</th>\n",
       "      <td>41184</td>\n",
       "      <td>7141</td>\n",
       "      <td>1.417000</td>\n",
       "      <td>3.96</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>179.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144840</th>\n",
       "      <td>41185</td>\n",
       "      <td>8113</td>\n",
       "      <td>0.775600</td>\n",
       "      <td>1.61</td>\n",
       "      <td>95.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>244.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144841</th>\n",
       "      <td>41186</td>\n",
       "      <td>5040</td>\n",
       "      <td>2.049400</td>\n",
       "      <td>1.26</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>259.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144842</th>\n",
       "      <td>41187</td>\n",
       "      <td>2946</td>\n",
       "      <td>0.080494</td>\n",
       "      <td>5.50</td>\n",
       "      <td>231.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1759.0</td>\n",
       "      <td>814.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144843</th>\n",
       "      <td>41188</td>\n",
       "      <td>8488</td>\n",
       "      <td>1.702100</td>\n",
       "      <td>4.13</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>161.533333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144844 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        order_id  rider_id  first_mile_distance  last_mile_distance  \\\n",
       "0         130231     12884             1.658500                4.54   \n",
       "1         130232      3541             2.070900                5.84   \n",
       "2         130233       603             1.388400                0.99   \n",
       "3         130234      3414             1.903900                2.59   \n",
       "4         130235      1426             0.827500                0.94   \n",
       "...          ...       ...                  ...                 ...   \n",
       "144839     41184      7141             1.417000                3.96   \n",
       "144840     41185      8113             0.775600                1.61   \n",
       "144841     41186      5040             2.049400                1.26   \n",
       "144842     41187      2946             0.080494                5.50   \n",
       "144843     41188      8488             1.702100                4.13   \n",
       "\n",
       "        alloted_orders  delivered_orders  undelivered_orders  \\\n",
       "0                216.0             215.0                 1.0   \n",
       "1                 52.0              52.0                 0.0   \n",
       "2                289.0             289.0                 0.0   \n",
       "3                125.0             122.0                 3.0   \n",
       "4                352.0             350.0                 2.0   \n",
       "...                ...               ...                 ...   \n",
       "144839            52.0              52.0                 0.0   \n",
       "144840            95.0              93.0                 2.0   \n",
       "144841            77.0              77.0                 0.0   \n",
       "144842           231.0             228.0                 3.0   \n",
       "144843            28.0              28.0                 0.0   \n",
       "\n",
       "        lifetime_order_count  session_time  \n",
       "0                      747.0    273.400000  \n",
       "1                       75.0    252.100000  \n",
       "2                     2214.0    241.383333  \n",
       "3                     1020.0    291.933333  \n",
       "4                     7284.0    247.133333  \n",
       "...                      ...           ...  \n",
       "144839                 413.0    179.266667  \n",
       "144840                 284.0    244.400000  \n",
       "144841                 119.0    259.000000  \n",
       "144842                1759.0    814.283333  \n",
       "144843                  43.0    161.533333  \n",
       "\n",
       "[144844 rows x 9 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>order_id</th>\n",
       "      <th>rider_id</th>\n",
       "      <th>first_mile_distance</th>\n",
       "      <th>last_mile_distance</th>\n",
       "      <th>alloted_orders</th>\n",
       "      <th>delivered_orders</th>\n",
       "      <th>undelivered_orders</th>\n",
       "      <th>lifetime_order_count</th>\n",
       "      <th>session_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144839</th>\n",
       "      <td>41184</td>\n",
       "      <td>7141</td>\n",
       "      <td>1.417000</td>\n",
       "      <td>3.96</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>179.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144840</th>\n",
       "      <td>41185</td>\n",
       "      <td>8113</td>\n",
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       "      <td>95.0</td>\n",
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       "      <td>284.0</td>\n",
       "      <td>244.400000</td>\n",
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       "    <tr>\n",
       "      <th>144841</th>\n",
       "      <td>41186</td>\n",
       "      <td>5040</td>\n",
       "      <td>2.049400</td>\n",
       "      <td>1.26</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>259.000000</td>\n",
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       "    <tr>\n",
       "      <th>144842</th>\n",
       "      <td>41187</td>\n",
       "      <td>2946</td>\n",
       "      <td>0.080494</td>\n",
       "      <td>5.50</td>\n",
       "      <td>231.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1759.0</td>\n",
       "      <td>814.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144843</th>\n",
       "      <td>41188</td>\n",
       "      <td>8488</td>\n",
       "      <td>1.702100</td>\n",
       "      <td>4.13</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>161.533333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        order_id  rider_id  first_mile_distance  last_mile_distance  \\\n",
       "144839     41184      7141             1.417000                3.96   \n",
       "144840     41185      8113             0.775600                1.61   \n",
       "144841     41186      5040             2.049400                1.26   \n",
       "144842     41187      2946             0.080494                5.50   \n",
       "144843     41188      8488             1.702100                4.13   \n",
       "\n",
       "        alloted_orders  delivered_orders  undelivered_orders  \\\n",
       "144839            52.0              52.0                 0.0   \n",
       "144840            95.0              93.0                 2.0   \n",
       "144841            77.0              77.0                 0.0   \n",
       "144842           231.0             228.0                 3.0   \n",
       "144843            28.0              28.0                 0.0   \n",
       "\n",
       "        lifetime_order_count  session_time  \n",
       "144839                 413.0    179.266667  \n",
       "144840                 284.0    244.400000  \n",
       "144841                 119.0    259.000000  \n",
       "144842                1759.0    814.283333  \n",
       "144843                  43.0    161.533333  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                   0\n",
       "rider_id                   0\n",
       "first_mile_distance        0\n",
       "last_mile_distance         0\n",
       "alloted_orders          4773\n",
       "delivered_orders        4884\n",
       "undelivered_orders      4884\n",
       "lifetime_order_count     778\n",
       "session_time            5054\n",
       "dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imp = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "imp = imp.fit(X_test[['alloted_orders','delivered_orders','undelivered_orders','lifetime_order_count','session_time']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[['alloted_orders','delivered_orders','undelivered_orders','lifetime_order_count','session_time']]=imp.transform(X_test[['alloted_orders','delivered_orders','undelivered_orders','lifetime_order_count','session_time']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>order_id</th>\n",
       "      <th>rider_id</th>\n",
       "      <th>first_mile_distance</th>\n",
       "      <th>last_mile_distance</th>\n",
       "      <th>alloted_orders</th>\n",
       "      <th>delivered_orders</th>\n",
       "      <th>undelivered_orders</th>\n",
       "      <th>lifetime_order_count</th>\n",
       "      <th>session_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>130231</td>\n",
       "      <td>12884</td>\n",
       "      <td>1.658500</td>\n",
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       "      <td>747.0</td>\n",
       "      <td>273.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>130232</td>\n",
       "      <td>3541</td>\n",
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       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>252.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>130233</td>\n",
       "      <td>603</td>\n",
       "      <td>1.388400</td>\n",
       "      <td>0.99</td>\n",
       "      <td>289.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>241.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>130234</td>\n",
       "      <td>3414</td>\n",
       "      <td>1.903900</td>\n",
       "      <td>2.59</td>\n",
       "      <td>125.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>291.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>130235</td>\n",
       "      <td>1426</td>\n",
       "      <td>0.827500</td>\n",
       "      <td>0.94</td>\n",
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       "      <th>144839</th>\n",
       "      <td>41184</td>\n",
       "      <td>7141</td>\n",
       "      <td>1.417000</td>\n",
       "      <td>3.96</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>179.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144840</th>\n",
       "      <td>41185</td>\n",
       "      <td>8113</td>\n",
       "      <td>0.775600</td>\n",
       "      <td>1.61</td>\n",
       "      <td>95.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>244.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144841</th>\n",
       "      <td>41186</td>\n",
       "      <td>5040</td>\n",
       "      <td>2.049400</td>\n",
       "      <td>1.26</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>259.000000</td>\n",
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       "    <tr>\n",
       "      <th>144842</th>\n",
       "      <td>41187</td>\n",
       "      <td>2946</td>\n",
       "      <td>0.080494</td>\n",
       "      <td>5.50</td>\n",
       "      <td>231.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1759.0</td>\n",
       "      <td>814.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144843</th>\n",
       "      <td>41188</td>\n",
       "      <td>8488</td>\n",
       "      <td>1.702100</td>\n",
       "      <td>4.13</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>161.533333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144844 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        order_id  rider_id  first_mile_distance  last_mile_distance  \\\n",
       "0         130231     12884             1.658500                4.54   \n",
       "1         130232      3541             2.070900                5.84   \n",
       "2         130233       603             1.388400                0.99   \n",
       "3         130234      3414             1.903900                2.59   \n",
       "4         130235      1426             0.827500                0.94   \n",
       "...          ...       ...                  ...                 ...   \n",
       "144839     41184      7141             1.417000                3.96   \n",
       "144840     41185      8113             0.775600                1.61   \n",
       "144841     41186      5040             2.049400                1.26   \n",
       "144842     41187      2946             0.080494                5.50   \n",
       "144843     41188      8488             1.702100                4.13   \n",
       "\n",
       "        alloted_orders  delivered_orders  undelivered_orders  \\\n",
       "0                216.0             215.0                 1.0   \n",
       "1                 52.0              52.0                 0.0   \n",
       "2                289.0             289.0                 0.0   \n",
       "3                125.0             122.0                 3.0   \n",
       "4                352.0             350.0                 2.0   \n",
       "...                ...               ...                 ...   \n",
       "144839            52.0              52.0                 0.0   \n",
       "144840            95.0              93.0                 2.0   \n",
       "144841            77.0              77.0                 0.0   \n",
       "144842           231.0             228.0                 3.0   \n",
       "144843            28.0              28.0                 0.0   \n",
       "\n",
       "        lifetime_order_count  session_time  \n",
       "0                      747.0    273.400000  \n",
       "1                       75.0    252.100000  \n",
       "2                     2214.0    241.383333  \n",
       "3                     1020.0    291.933333  \n",
       "4                     7284.0    247.133333  \n",
       "...                      ...           ...  \n",
       "144839                 413.0    179.266667  \n",
       "144840                 284.0    244.400000  \n",
       "144841                 119.0    259.000000  \n",
       "144842                1759.0    814.283333  \n",
       "144843                  43.0    161.533333  \n",
       "\n",
       "[144844 rows x 9 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                0\n",
       "rider_id                0\n",
       "first_mile_distance     0\n",
       "last_mile_distance      0\n",
       "alloted_orders          0\n",
       "delivered_orders        0\n",
       "undelivered_orders      0\n",
       "lifetime_order_count    0\n",
       "session_time            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.isnull().sum()"
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   "execution_count": 150,
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   "outputs": [
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       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
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     "execution_count": 150,
     "metadata": {},
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   "source": [
    "dtc_new.predict(X_test)"
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  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test=dtc_new.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
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  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc_new.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144844,)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test['order_id'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144844,)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "final=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "final['order_id']=X_test['order_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "final['cancelled']=y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
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   "source": [
    "final"
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   "cell_type": "code",
   "execution_count": 160,
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   "outputs": [
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       "        order_id  cancelled\n",
       "0         130231          0\n",
       "1         130232          0\n",
       "2         130233          0\n",
       "3         130234          0\n",
       "4         130235          0\n",
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       "144842     41187          0\n",
       "144843     41188          0\n",
       "\n",
       "[144844 rows x 2 columns]"
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     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "final.to_csv('submit_1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                0\n",
       "rider_id                0\n",
       "first_mile_distance     0\n",
       "last_mile_distance      0\n",
       "alloted_orders          0\n",
       "delivered_orders        0\n",
       "undelivered_orders      0\n",
       "lifetime_order_count    0\n",
       "session_time            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.isnull().sum()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "source": []
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   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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