airbnb_train_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nprint(df_train.country_destination.unique())\\n\\nprint(\"=============\")\\n\\nprint((df_train.country_destination == \\'US\\').sum())\\nprint((df_train.country_destination == \\'FR\\').sum())\\nprint((df_train.country_destination == \\'CA\\').sum())\\nprint((df_train.country_destination == \\'GB\\').sum())\\nprint((df_train.country_destination == \\'ES\\').sum())\\nprint((df_train.country_destination == \\'IT\\').sum())\\nprint((df_train.country_destination == \\'PT\\').sum())\\nprint((df_train.country_destination == \\'NL\\').sum())\\nprint((df_train.country_destination == \\'DE\\').sum())\\nprint((df_train.country_destination == \\'AU\\').sum())\\n\\nprint(\"==============\")\\nprint(df_train[\\'country_destination\\'].describe())\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('/home/sanjana/Desktop/datasets/airbnb-recruiting-new-user-bookings/train_users_2.csv')\n",
    "#df_test = pd.read_csv('/home/sanjana/Desktop/datasets/airbnb-recruiting-new-user-bookings/train_users_2.csv')\n",
    "sessions = pd.read_csv('/home/sanjana/Desktop/datasets/airbnb-recruiting-new-user-bookings/sessions.csv')\n",
    "#usergrp = pd.read_csv('')\n",
    "#countries = pd.read_csv('')\n",
    "'''\n",
    "print(df_train.country_destination.unique())\n",
    "\n",
    "print(\"=============\")\n",
    "\n",
    "print((df_train.country_destination == 'US').sum())\n",
    "print((df_train.country_destination == 'FR').sum())\n",
    "print((df_train.country_destination == 'CA').sum())\n",
    "print((df_train.country_destination == 'GB').sum())\n",
    "print((df_train.country_destination == 'ES').sum())\n",
    "print((df_train.country_destination == 'IT').sum())\n",
    "print((df_train.country_destination == 'PT').sum())\n",
    "print((df_train.country_destination == 'NL').sum())\n",
    "print((df_train.country_destination == 'DE').sum())\n",
    "print((df_train.country_destination == 'AU').sum())\n",
    "\n",
    "print(\"==============\")\n",
    "print(df_train['country_destination'].describe())\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10567737 entries, 0 to 10567736\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Dtype  \n",
      "---  ------         -----  \n",
      " 0   user_id        object \n",
      " 1   action         object \n",
      " 2   action_type    object \n",
      " 3   action_detail  object \n",
      " 4   device_type    object \n",
      " 5   secs_elapsed   float64\n",
      "dtypes: float64(1), object(5)\n",
      "memory usage: 483.8+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>secs_elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>319.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>67753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>22141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>435.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  secs_elapsed\n",
       "0  d1mm9tcy42         319.0\n",
       "1  d1mm9tcy42       67753.0\n",
       "2  d1mm9tcy42         301.0\n",
       "3  d1mm9tcy42       22141.0\n",
       "4  d1mm9tcy42         435.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(sessions.info())\n",
    "sessions.dropna(subset = [\"user_id\"], inplace=True)\n",
    "sessions.isnull().sum()\n",
    "sessions = sessions.drop(['action', 'action_type', 'action_detail', 'device_type'], axis = 1)\n",
    "sessions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sessions1=sessions.groupby('user_id')['secs_elapsed'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "00023iyk9l     867896.0\n",
       "0010k6l0om     586543.0\n",
       "001wyh0pz8     282965.0\n",
       "0028jgx1x1     297010.0\n",
       "002qnbzfs5    6487080.0\n",
       "                ...    \n",
       "zzxox7jnrx     639436.0\n",
       "zzy7t0y9cm      73771.0\n",
       "zzysuoqg6x       1731.0\n",
       "zzywmcn0jv    2149949.0\n",
       "zzzlylp57e     430959.0\n",
       "Name: secs_elapsed, Length: 135483, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sessions1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "135483\n"
     ]
    }
   ],
   "source": [
    "sessions = pd.DataFrame(sessions1)\n",
    "user_id = list(sessions.index.values)  # this will always work in pandas\n",
    "print(len(user_id))\n",
    "sessions['id'] = user_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>secs_elapsed</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
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       "      <td>282965.0</td>\n",
       "      <td>001wyh0pz8</td>\n",
       "    </tr>\n",
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       "      <th>0028jgx1x1</th>\n",
       "      <td>297010.0</td>\n",
       "      <td>0028jgx1x1</td>\n",
       "    </tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            secs_elapsed          id\n",
       "user_id                             \n",
       "00023iyk9l      867896.0  00023iyk9l\n",
       "0010k6l0om      586543.0  0010k6l0om\n",
       "001wyh0pz8      282965.0  001wyh0pz8\n",
       "0028jgx1x1      297010.0  0028jgx1x1\n",
       "002qnbzfs5     6487080.0  002qnbzfs5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sessions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>secs_elapsed</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>867896.0</td>\n",
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       "      <td>586543.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>282965.0</td>\n",
       "      <td>001wyh0pz8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>297010.0</td>\n",
       "      <td>0028jgx1x1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6487080.0</td>\n",
       "      <td>002qnbzfs5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   secs_elapsed          id\n",
       "0      867896.0  00023iyk9l\n",
       "1      586543.0  0010k6l0om\n",
       "2      282965.0  001wyh0pz8\n",
       "3      297010.0  0028jgx1x1\n",
       "4     6487080.0  002qnbzfs5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sessions.reset_index(drop=True, inplace=True)\n",
    "sessions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date_account_created</th>\n",
       "      <th>timestamp_first_active</th>\n",
       "      <th>date_first_booking</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>signup_method</th>\n",
       "      <th>signup_flow</th>\n",
       "      <th>language</th>\n",
       "      <th>affiliate_channel</th>\n",
       "      <th>affiliate_provider</th>\n",
       "      <th>first_affiliate_tracked</th>\n",
       "      <th>signup_app</th>\n",
       "      <th>first_device_type</th>\n",
       "      <th>first_browser</th>\n",
       "      <th>country_destination</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gxn3p5htnn</td>\n",
       "      <td>2010-06-28</td>\n",
       "      <td>20090319043255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>NDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>820tgsjxq7</td>\n",
       "      <td>2011-05-25</td>\n",
       "      <td>20090523174809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MALE</td>\n",
       "      <td>38.0</td>\n",
       "      <td>facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>seo</td>\n",
       "      <td>google</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>NDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4ft3gnwmtx</td>\n",
       "      <td>2010-09-28</td>\n",
       "      <td>20090609231247</td>\n",
       "      <td>2010-08-02</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>56.0</td>\n",
       "      <td>basic</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>IE</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bjjt8pjhuk</td>\n",
       "      <td>2011-12-05</td>\n",
       "      <td>20091031060129</td>\n",
       "      <td>2012-09-08</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>42.0</td>\n",
       "      <td>facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87mebub9p4</td>\n",
       "      <td>2010-09-14</td>\n",
       "      <td>20091208061105</td>\n",
       "      <td>2010-02-18</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>41.0</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id date_account_created  timestamp_first_active date_first_booking  \\\n",
       "0  gxn3p5htnn           2010-06-28          20090319043255                NaN   \n",
       "1  820tgsjxq7           2011-05-25          20090523174809                NaN   \n",
       "2  4ft3gnwmtx           2010-09-28          20090609231247         2010-08-02   \n",
       "3  bjjt8pjhuk           2011-12-05          20091031060129         2012-09-08   \n",
       "4  87mebub9p4           2010-09-14          20091208061105         2010-02-18   \n",
       "\n",
       "      gender   age signup_method  signup_flow language affiliate_channel  \\\n",
       "0  -unknown-   NaN      facebook            0       en            direct   \n",
       "1       MALE  38.0      facebook            0       en               seo   \n",
       "2     FEMALE  56.0         basic            3       en            direct   \n",
       "3     FEMALE  42.0      facebook            0       en            direct   \n",
       "4  -unknown-  41.0         basic            0       en            direct   \n",
       "\n",
       "  affiliate_provider first_affiliate_tracked signup_app first_device_type  \\\n",
       "0             direct               untracked        Web       Mac Desktop   \n",
       "1             google               untracked        Web       Mac Desktop   \n",
       "2             direct               untracked        Web   Windows Desktop   \n",
       "3             direct               untracked        Web       Mac Desktop   \n",
       "4             direct               untracked        Web       Mac Desktop   \n",
       "\n",
       "  first_browser country_destination  \n",
       "0        Chrome                 NDF  \n",
       "1        Chrome                 NDF  \n",
       "2            IE                  US  \n",
       "3       Firefox               other  \n",
       "4        Chrome                  US  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.merge(df_train, sessions, how='inner', on = 'id')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date_account_created</th>\n",
       "      <th>timestamp_first_active</th>\n",
       "      <th>date_first_booking</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>signup_method</th>\n",
       "      <th>signup_flow</th>\n",
       "      <th>language</th>\n",
       "      <th>affiliate_channel</th>\n",
       "      <th>affiliate_provider</th>\n",
       "      <th>first_affiliate_tracked</th>\n",
       "      <th>signup_app</th>\n",
       "      <th>first_device_type</th>\n",
       "      <th>first_browser</th>\n",
       "      <th>country_destination</th>\n",
       "      <th>secs_elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20140101000936</td>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>MALE</td>\n",
       "      <td>62.0</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>sem-non-brand</td>\n",
       "      <td>google</td>\n",
       "      <td>omg</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>other</td>\n",
       "      <td>3427529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yo8nz8bqcq</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20140101001558</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>NDF</td>\n",
       "      <td>207842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4grx6yxeby</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20140101001639</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>sem-brand</td>\n",
       "      <td>google</td>\n",
       "      <td>omg</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>NDF</td>\n",
       "      <td>1135444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ncf87guaf0</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20140101002146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>linked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>NDF</td>\n",
       "      <td>3755100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4rvqpxoh3h</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20140101002619</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>basic</td>\n",
       "      <td>25</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>iOS</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>GB</td>\n",
       "      <td>2555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>73810</th>\n",
       "      <td>zxodksqpep</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>20140630235636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MALE</td>\n",
       "      <td>32.0</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>sem-brand</td>\n",
       "      <td>google</td>\n",
       "      <td>omg</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Safari</td>\n",
       "      <td>NDF</td>\n",
       "      <td>5141396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73811</th>\n",
       "      <td>mhewnxesx9</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>20140630235719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>linked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>NDF</td>\n",
       "      <td>2878924.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73812</th>\n",
       "      <td>6o3arsjbb4</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>20140630235754</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>32.0</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>NDF</td>\n",
       "      <td>342982.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73813</th>\n",
       "      <td>jh95kwisub</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>20140630235822</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>basic</td>\n",
       "      <td>25</td>\n",
       "      <td>en</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>tracked-other</td>\n",
       "      <td>iOS</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>Mobile Safari</td>\n",
       "      <td>NDF</td>\n",
       "      <td>341609.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73814</th>\n",
       "      <td>nw9fwlyb5f</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>20140630235824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>basic</td>\n",
       "      <td>25</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>iOS</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NDF</td>\n",
       "      <td>2759210.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73815 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id date_account_created  timestamp_first_active  \\\n",
       "0      d1mm9tcy42           2014-01-01          20140101000936   \n",
       "1      yo8nz8bqcq           2014-01-01          20140101001558   \n",
       "2      4grx6yxeby           2014-01-01          20140101001639   \n",
       "3      ncf87guaf0           2014-01-01          20140101002146   \n",
       "4      4rvqpxoh3h           2014-01-01          20140101002619   \n",
       "...           ...                  ...                     ...   \n",
       "73810  zxodksqpep           2014-06-30          20140630235636   \n",
       "73811  mhewnxesx9           2014-06-30          20140630235719   \n",
       "73812  6o3arsjbb4           2014-06-30          20140630235754   \n",
       "73813  jh95kwisub           2014-06-30          20140630235822   \n",
       "73814  nw9fwlyb5f           2014-06-30          20140630235824   \n",
       "\n",
       "      date_first_booking     gender   age signup_method  signup_flow language  \\\n",
       "0             2014-01-04       MALE  62.0         basic            0       en   \n",
       "1                    NaN  -unknown-   NaN         basic            0       en   \n",
       "2                    NaN  -unknown-   NaN         basic            0       en   \n",
       "3                    NaN  -unknown-   NaN         basic            0       en   \n",
       "4             2014-01-02  -unknown-   NaN         basic           25       en   \n",
       "...                  ...        ...   ...           ...          ...      ...   \n",
       "73810                NaN       MALE  32.0         basic            0       en   \n",
       "73811                NaN  -unknown-   NaN         basic            0       en   \n",
       "73812                NaN  -unknown-  32.0         basic            0       en   \n",
       "73813                NaN  -unknown-   NaN         basic           25       en   \n",
       "73814                NaN  -unknown-   NaN         basic           25       en   \n",
       "\n",
       "      affiliate_channel affiliate_provider first_affiliate_tracked signup_app  \\\n",
       "0         sem-non-brand             google                     omg        Web   \n",
       "1                direct             direct               untracked        Web   \n",
       "2             sem-brand             google                     omg        Web   \n",
       "3                direct             direct                  linked        Web   \n",
       "4                direct             direct               untracked        iOS   \n",
       "...                 ...                ...                     ...        ...   \n",
       "73810         sem-brand             google                     omg        Web   \n",
       "73811            direct             direct                  linked        Web   \n",
       "73812            direct             direct               untracked        Web   \n",
       "73813             other              other           tracked-other        iOS   \n",
       "73814            direct             direct               untracked        iOS   \n",
       "\n",
       "      first_device_type  first_browser country_destination  secs_elapsed  \n",
       "0       Windows Desktop         Chrome               other     3427529.0  \n",
       "1           Mac Desktop        Firefox                 NDF      207842.0  \n",
       "2       Windows Desktop        Firefox                 NDF     1135444.0  \n",
       "3       Windows Desktop         Chrome                 NDF     3755100.0  \n",
       "4                iPhone      -unknown-                  GB        2555.0  \n",
       "...                 ...            ...                 ...           ...  \n",
       "73810       Mac Desktop         Safari                 NDF     5141396.0  \n",
       "73811   Windows Desktop         Chrome                 NDF     2878924.0  \n",
       "73812       Mac Desktop        Firefox                 NDF      342982.0  \n",
       "73813            iPhone  Mobile Safari                 NDF      341609.0  \n",
       "73814            iPhone      -unknown-                 NDF     2759210.0  \n",
       "\n",
       "[73815 rows x 17 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ncount=[62376,5023,1428,2324,2249,2835,217,762,1061,539]\\ncnty=['US', 'FR', 'CA', 'GB', 'ES', 'IT', 'PT', 'NL', 'DE', 'AU']\\nprint(count)\\nprint(cnty)\\nfrom matplotlib import pyplot as plt\\nplt.title('Distribution of User preferred Destinations')\\nplt.xlabel('Countries')\\nplt.ylabel('Bookings')\\nplt.bar(cnty, count)\\n\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "count=[62376,5023,1428,2324,2249,2835,217,762,1061,539]\n",
    "cnty=['US', 'FR', 'CA', 'GB', 'ES', 'IT', 'PT', 'NL', 'DE', 'AU']\n",
    "print(count)\n",
    "print(cnty)\n",
    "from matplotlib import pyplot as plt\n",
    "plt.title('Distribution of User preferred Destinations')\n",
    "plt.xlabel('Countries')\n",
    "plt.ylabel('Bookings')\n",
    "plt.bar(cnty, count)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                             0\n",
       "date_account_created           0\n",
       "timestamp_first_active         0\n",
       "date_first_booking         45041\n",
       "gender                         0\n",
       "age                        32248\n",
       "signup_method                  0\n",
       "signup_flow                    0\n",
       "language                       0\n",
       "affiliate_channel              0\n",
       "affiliate_provider             0\n",
       "first_affiliate_tracked      302\n",
       "signup_app                     0\n",
       "first_device_type              0\n",
       "first_browser                  0\n",
       "country_destination            0\n",
       "secs_elapsed                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               id date_account_created  timestamp_first_active  \\\n",
      "0      d1mm9tcy42           2014-01-01          20140101000936   \n",
      "1      yo8nz8bqcq           2014-01-01          20140101001558   \n",
      "2      4grx6yxeby           2014-01-01          20140101001639   \n",
      "3      ncf87guaf0           2014-01-01          20140101002146   \n",
      "4      4rvqpxoh3h           2014-01-01          20140101002619   \n",
      "...           ...                  ...                     ...   \n",
      "73810  zxodksqpep           2014-06-30          20140630235636   \n",
      "73811  mhewnxesx9           2014-06-30          20140630235719   \n",
      "73812  6o3arsjbb4           2014-06-30          20140630235754   \n",
      "73813  jh95kwisub           2014-06-30          20140630235822   \n",
      "73814  nw9fwlyb5f           2014-06-30          20140630235824   \n",
      "\n",
      "      date_first_booking     gender   age signup_method  signup_flow language  \\\n",
      "0             2014-01-04       MALE  62.0         basic            0       en   \n",
      "1                    NaN  -unknown-   NaN         basic            0       en   \n",
      "2                    NaN  -unknown-   NaN         basic            0       en   \n",
      "3                    NaN  -unknown-   NaN         basic            0       en   \n",
      "4             2014-01-02  -unknown-   NaN         basic           25       en   \n",
      "...                  ...        ...   ...           ...          ...      ...   \n",
      "73810                NaN       MALE  32.0         basic            0       en   \n",
      "73811                NaN  -unknown-   NaN         basic            0       en   \n",
      "73812                NaN  -unknown-  32.0         basic            0       en   \n",
      "73813                NaN  -unknown-   NaN         basic           25       en   \n",
      "73814                NaN  -unknown-   NaN         basic           25       en   \n",
      "\n",
      "      affiliate_channel affiliate_provider first_affiliate_tracked signup_app  \\\n",
      "0         sem-non-brand             google                     omg        Web   \n",
      "1                direct             direct               untracked        Web   \n",
      "2             sem-brand             google                     omg        Web   \n",
      "3                direct             direct                  linked        Web   \n",
      "4                direct             direct               untracked        iOS   \n",
      "...                 ...                ...                     ...        ...   \n",
      "73810         sem-brand             google                     omg        Web   \n",
      "73811            direct             direct                  linked        Web   \n",
      "73812            direct             direct               untracked        Web   \n",
      "73813             other              other           tracked-other        iOS   \n",
      "73814            direct             direct               untracked        iOS   \n",
      "\n",
      "      first_device_type  first_browser country_destination  secs_elapsed  \n",
      "0       Windows Desktop         Chrome               other     3427529.0  \n",
      "1           Mac Desktop        Firefox                 NDF      207842.0  \n",
      "2       Windows Desktop        Firefox                 NDF     1135444.0  \n",
      "3       Windows Desktop         Chrome                 NDF     3755100.0  \n",
      "4                iPhone      -unknown-                  GB        2555.0  \n",
      "...                 ...            ...                 ...           ...  \n",
      "73810       Mac Desktop         Safari                 NDF     5141396.0  \n",
      "73811   Windows Desktop         Chrome                 NDF     2878924.0  \n",
      "73812       Mac Desktop        Firefox                 NDF      342982.0  \n",
      "73813            iPhone  Mobile Safari                 NDF      341609.0  \n",
      "73814            iPhone      -unknown-                 NDF     2759210.0  \n",
      "\n",
      "[73815 rows x 17 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "untracked        39049\n",
       "omg              18342\n",
       "linked           14316\n",
       "tracked-other      938\n",
       "product            733\n",
       "marketing          110\n",
       "local ops           25\n",
       "Name: first_affiliate_tracked, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['first_affiliate_tracked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>timestamp_first_active</th>\n",
       "      <th>age</th>\n",
       "      <th>signup_flow</th>\n",
       "      <th>secs_elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.381500e+04</td>\n",
       "      <td>41567.000000</td>\n",
       "      <td>73815.000000</td>\n",
       "      <td>7.381500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.014040e+13</td>\n",
       "      <td>42.414993</td>\n",
       "      <td>4.856208</td>\n",
       "      <td>1.514235e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.711447e+08</td>\n",
       "      <td>111.201954</td>\n",
       "      <td>9.339480</td>\n",
       "      <td>1.913191e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.014010e+13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.014023e+13</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.569205e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.014042e+13</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.728620e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.014053e+13</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.043488e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.014063e+13</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.822136e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       timestamp_first_active           age   signup_flow  secs_elapsed\n",
       "count            7.381500e+04  41567.000000  73815.000000  7.381500e+04\n",
       "mean             2.014040e+13     42.414993      4.856208  1.514235e+06\n",
       "std              1.711447e+08    111.201954      9.339480  1.913191e+06\n",
       "min              2.014010e+13      1.000000      0.000000  0.000000e+00\n",
       "25%              2.014023e+13     27.000000      0.000000  2.569205e+05\n",
       "50%              2.014042e+13     32.000000      0.000000  8.728620e+05\n",
       "75%              2.014053e+13     41.000000      0.000000  2.043488e+06\n",
       "max              2.014063e+13   2014.000000     25.000000  3.822136e+07"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['first_affiliate_tracked'].fillna(\"untracked\",inplace = True)\n",
    "\n",
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date_account_created</th>\n",
       "      <th>timestamp_first_active</th>\n",
       "      <th>date_first_booking</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>signup_method</th>\n",
       "      <th>signup_flow</th>\n",
       "      <th>language</th>\n",
       "      <th>affiliate_channel</th>\n",
       "      <th>affiliate_provider</th>\n",
       "      <th>first_affiliate_tracked</th>\n",
       "      <th>signup_app</th>\n",
       "      <th>first_device_type</th>\n",
       "      <th>first_browser</th>\n",
       "      <th>country_destination</th>\n",
       "      <th>secs_elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20140101000936</td>\n",
       "      <td>1</td>\n",
       "      <td>MALE</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>sem-non-brand</td>\n",
       "      <td>google</td>\n",
       "      <td>omg</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>other</td>\n",
       "      <td>3427529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yo8nz8bqcq</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20140101001558</td>\n",
       "      <td>0</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>NDF</td>\n",
       "      <td>207842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4grx6yxeby</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20140101001639</td>\n",
       "      <td>0</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>sem-brand</td>\n",
       "      <td>google</td>\n",
       "      <td>omg</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>NDF</td>\n",
       "      <td>1135444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ncf87guaf0</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20140101002146</td>\n",
       "      <td>0</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>linked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>NDF</td>\n",
       "      <td>3755100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4rvqpxoh3h</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20140101002619</td>\n",
       "      <td>1</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>basic</td>\n",
       "      <td>25</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>iOS</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>GB</td>\n",
       "      <td>2555.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id date_account_created  timestamp_first_active  \\\n",
       "0  d1mm9tcy42           2014-01-01          20140101000936   \n",
       "1  yo8nz8bqcq           2014-01-01          20140101001558   \n",
       "2  4grx6yxeby           2014-01-01          20140101001639   \n",
       "3  ncf87guaf0           2014-01-01          20140101002146   \n",
       "4  4rvqpxoh3h           2014-01-01          20140101002619   \n",
       "\n",
       "   date_first_booking     gender        age signup_method  signup_flow  \\\n",
       "0                   1       MALE  62.000000         basic            0   \n",
       "1                   0  -unknown-  36.234347         basic            0   \n",
       "2                   0  -unknown-  36.234347         basic            0   \n",
       "3                   0  -unknown-  36.234347         basic            0   \n",
       "4                   1  -unknown-  36.234347         basic           25   \n",
       "\n",
       "  language affiliate_channel affiliate_provider first_affiliate_tracked  \\\n",
       "0       en     sem-non-brand             google                     omg   \n",
       "1       en            direct             direct               untracked   \n",
       "2       en         sem-brand             google                     omg   \n",
       "3       en            direct             direct                  linked   \n",
       "4       en            direct             direct               untracked   \n",
       "\n",
       "  signup_app first_device_type first_browser country_destination  secs_elapsed  \n",
       "0        Web   Windows Desktop        Chrome               other     3427529.0  \n",
       "1        Web       Mac Desktop       Firefox                 NDF      207842.0  \n",
       "2        Web   Windows Desktop       Firefox                 NDF     1135444.0  \n",
       "3        Web   Windows Desktop        Chrome                 NDF     3755100.0  \n",
       "4        iOS            iPhone     -unknown-                  GB        2555.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.drop(df_train[df_train['age'] > 120].index, inplace = True)\n",
    "df_train.drop(df_train[df_train['age'] < 18].index, inplace = True)\n",
    "df_train['age'].describe()\n",
    "df_train['age'].fillna(df_train['age'].mean(),inplace = True)\n",
    "\n",
    "\n",
    "df_train['date_first_booking'] = df_train['date_first_booking'].apply(lambda x: 1 if not pd.isnull(x) else 0)\n",
    "df_train['age'].fillna(df_train['age'].mean(),inplace = True)\n",
    "\n",
    "df_train.isnull().sum()\n",
    "\n",
    "df_train['date_first_booking'].head()\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_train['date_account_created']=pd.to_datetime(df_train[\"date_account_created\"])\n",
    "df_train=df_train.drop(['date_account_created'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>timestamp_first_active</th>\n",
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       "      <th>signup_flow</th>\n",
       "      <th>language</th>\n",
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       "      <th>affiliate_provider</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>Web</td>\n",
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       "      <td>Chrome</td>\n",
       "      <td>other</td>\n",
       "      <td>3427529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20140101001558</td>\n",
       "      <td>0</td>\n",
       "      <td>-unknown-</td>\n",
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       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
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       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>NDF</td>\n",
       "      <td>207842.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20140101001639</td>\n",
       "      <td>0</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>sem-brand</td>\n",
       "      <td>google</td>\n",
       "      <td>omg</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>NDF</td>\n",
       "      <td>1135444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20140101002146</td>\n",
       "      <td>0</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>linked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>NDF</td>\n",
       "      <td>3755100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20140101002619</td>\n",
       "      <td>1</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>basic</td>\n",
       "      <td>25</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>iOS</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>GB</td>\n",
       "      <td>2555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>73810</th>\n",
       "      <td>20140630235636</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>sem-brand</td>\n",
       "      <td>google</td>\n",
       "      <td>omg</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Safari</td>\n",
       "      <td>NDF</td>\n",
       "      <td>5141396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73811</th>\n",
       "      <td>20140630235719</td>\n",
       "      <td>0</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>linked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>NDF</td>\n",
       "      <td>2878924.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73812</th>\n",
       "      <td>20140630235754</td>\n",
       "      <td>0</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>NDF</td>\n",
       "      <td>342982.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73813</th>\n",
       "      <td>20140630235822</td>\n",
       "      <td>0</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>basic</td>\n",
       "      <td>25</td>\n",
       "      <td>en</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>tracked-other</td>\n",
       "      <td>iOS</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>Mobile Safari</td>\n",
       "      <td>NDF</td>\n",
       "      <td>341609.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73814</th>\n",
       "      <td>20140630235824</td>\n",
       "      <td>0</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>basic</td>\n",
       "      <td>25</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>iOS</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NDF</td>\n",
       "      <td>2759210.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73631 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       timestamp_first_active  date_first_booking     gender        age  \\\n",
       "0              20140101000936                   1       MALE  62.000000   \n",
       "1              20140101001558                   0  -unknown-  36.234347   \n",
       "2              20140101001639                   0  -unknown-  36.234347   \n",
       "3              20140101002146                   0  -unknown-  36.234347   \n",
       "4              20140101002619                   1  -unknown-  36.234347   \n",
       "...                       ...                 ...        ...        ...   \n",
       "73810          20140630235636                   0       MALE  32.000000   \n",
       "73811          20140630235719                   0  -unknown-  36.234347   \n",
       "73812          20140630235754                   0  -unknown-  32.000000   \n",
       "73813          20140630235822                   0  -unknown-  36.234347   \n",
       "73814          20140630235824                   0  -unknown-  36.234347   \n",
       "\n",
       "      signup_method  signup_flow language affiliate_channel  \\\n",
       "0             basic            0       en     sem-non-brand   \n",
       "1             basic            0       en            direct   \n",
       "2             basic            0       en         sem-brand   \n",
       "3             basic            0       en            direct   \n",
       "4             basic           25       en            direct   \n",
       "...             ...          ...      ...               ...   \n",
       "73810         basic            0       en         sem-brand   \n",
       "73811         basic            0       en            direct   \n",
       "73812         basic            0       en            direct   \n",
       "73813         basic           25       en             other   \n",
       "73814         basic           25       en            direct   \n",
       "\n",
       "      affiliate_provider first_affiliate_tracked signup_app first_device_type  \\\n",
       "0                 google                     omg        Web   Windows Desktop   \n",
       "1                 direct               untracked        Web       Mac Desktop   \n",
       "2                 google                     omg        Web   Windows Desktop   \n",
       "3                 direct                  linked        Web   Windows Desktop   \n",
       "4                 direct               untracked        iOS            iPhone   \n",
       "...                  ...                     ...        ...               ...   \n",
       "73810             google                     omg        Web       Mac Desktop   \n",
       "73811             direct                  linked        Web   Windows Desktop   \n",
       "73812             direct               untracked        Web       Mac Desktop   \n",
       "73813              other           tracked-other        iOS            iPhone   \n",
       "73814             direct               untracked        iOS            iPhone   \n",
       "\n",
       "       first_browser country_destination  secs_elapsed  \n",
       "0             Chrome               other     3427529.0  \n",
       "1            Firefox                 NDF      207842.0  \n",
       "2            Firefox                 NDF     1135444.0  \n",
       "3             Chrome                 NDF     3755100.0  \n",
       "4          -unknown-                  GB        2555.0  \n",
       "...              ...                 ...           ...  \n",
       "73810         Safari                 NDF     5141396.0  \n",
       "73811         Chrome                 NDF     2878924.0  \n",
       "73812        Firefox                 NDF      342982.0  \n",
       "73813  Mobile Safari                 NDF      341609.0  \n",
       "73814      -unknown-                 NDF     2759210.0  \n",
       "\n",
       "[73631 rows x 15 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train=df_train.drop(['id'],axis=1)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  country_destination signup_method  date_first_booking        age     gender  \\\n",
      "0               other         basic                   1  62.000000       MALE   \n",
      "1                 NDF         basic                   0  36.234347  -unknown-   \n",
      "2                 NDF         basic                   0  36.234347  -unknown-   \n",
      "3                 NDF         basic                   0  36.234347  -unknown-   \n",
      "4                  GB         basic                   1  36.234347  -unknown-   \n",
      "\n",
      "  language  secs_elapsed  \n",
      "0       en     3427529.0  \n",
      "1       en      207842.0  \n",
      "2       en     1135444.0  \n",
      "3       en     3755100.0  \n",
      "4       en        2555.0  \n"
     ]
    }
   ],
   "source": [
    "df_train_names=df_train\n",
    "df_train_names.head()\n",
    "new_train_ann= df_train[['country_destination', 'signup_method','date_first_booking', 'age','gender',\n",
    "                      'language', 'secs_elapsed']].copy()\n",
    "print(new_train_ann.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>timestamp_first_active</th>\n",
       "      <th>date_first_booking</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>signup_method</th>\n",
       "      <th>signup_flow</th>\n",
       "      <th>language</th>\n",
       "      <th>affiliate_channel</th>\n",
       "      <th>affiliate_provider</th>\n",
       "      <th>first_affiliate_tracked</th>\n",
       "      <th>signup_app</th>\n",
       "      <th>first_device_type</th>\n",
       "      <th>first_browser</th>\n",
       "      <th>country_destination</th>\n",
       "      <th>secs_elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20140101000936</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>207842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20140101001639</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
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       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1135444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3755100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20140101002619</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2555.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestamp_first_active  date_first_booking  gender        age  \\\n",
       "0          20140101000936                   1       2  62.000000   \n",
       "1          20140101001558                   0       0  36.234347   \n",
       "2          20140101001639                   0       0  36.234347   \n",
       "3          20140101002146                   0       0  36.234347   \n",
       "4          20140101002619                   1       0  36.234347   \n",
       "\n",
       "   signup_method  signup_flow  language  affiliate_channel  \\\n",
       "0              0            0         5                  6   \n",
       "1              0            0         5                  2   \n",
       "2              0            0         5                  5   \n",
       "3              0            0         5                  2   \n",
       "4              0           25         5                  2   \n",
       "\n",
       "   affiliate_provider  first_affiliate_tracked  signup_app  first_device_type  \\\n",
       "0                   8                        3           2                  6   \n",
       "1                   4                        6           2                  3   \n",
       "2                   8                        3           2                  6   \n",
       "3                   4                        0           2                  6   \n",
       "4                   4                        6           3                  8   \n",
       "\n",
       "   first_browser  country_destination  secs_elapsed  \n",
       "0              6                   11     3427529.0  \n",
       "1             10                    7      207842.0  \n",
       "2             10                    7     1135444.0  \n",
       "3              6                    7     3755100.0  \n",
       "4              0                    5        2555.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "gender=LabelEncoder()\n",
    "df_train['gender']=gender.fit_transform(df_train['gender'])\n",
    "language=LabelEncoder()\n",
    "df_train['language']=language.fit_transform(df_train['language'])\n",
    "affiliate_channel=LabelEncoder()\n",
    "df_train['affiliate_channel']=affiliate_channel.fit_transform(df_train['affiliate_channel'])\n",
    "affiliate_provider=LabelEncoder()\n",
    "df_train['affiliate_provider']=affiliate_provider.fit_transform(df_train['affiliate_provider'])\n",
    "first_affiliate_tracked=LabelEncoder()\n",
    "df_train['first_affiliate_tracked']=first_affiliate_tracked.fit_transform(df_train['first_affiliate_tracked'])\n",
    "signup_app=LabelEncoder()\n",
    "df_train['signup_app']=signup_app.fit_transform(df_train['signup_app'])\n",
    "signup_method=LabelEncoder()\n",
    "df_train['signup_method']=signup_method.fit_transform(df_train['signup_method'])\n",
    "first_device_type=LabelEncoder()\n",
    "df_train['first_device_type']=first_device_type.fit_transform(df_train['first_device_type'])\n",
    "\n",
    "country_destination=LabelEncoder()\n",
    "df_train['country_destination']=country_destination.fit_transform(df_train['country_destination'])\n",
    "\n",
    "first_browser=LabelEncoder()\n",
    "df_train['first_browser']=first_browser.fit_transform(df_train['first_browser'])\n",
    "df_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "gender = le.fit_transform(list(df_train[\"gender\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_name=\"country_destination\"\n",
    "first_col=df_train.pop(col_name)\n",
    "df_train.insert(0, col_name, first_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['timestamp_first_active', 'date_first_booking', 'gender', 'age',\n",
      "       'signup_method', 'signup_flow', 'language', 'affiliate_channel',\n",
      "       'affiliate_provider', 'first_affiliate_tracked', 'signup_app',\n",
      "       'first_device_type', 'first_browser', 'secs_elapsed'],\n",
      "      dtype='object')\n",
      "44178 29453 44178 29453\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df_train.iloc[:,1:18]\n",
    "Y = df_train.iloc[:,0]\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.4, random_state = 0)\n",
    "print(x_train.columns)\n",
    "print(len(x_train),len(x_test),len(y_train),len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_destination</th>\n",
       "      <th>timestamp_first_active</th>\n",
       "      <th>date_first_booking</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>signup_method</th>\n",
       "      <th>signup_flow</th>\n",
       "      <th>language</th>\n",
       "      <th>affiliate_channel</th>\n",
       "      <th>affiliate_provider</th>\n",
       "      <th>first_affiliate_tracked</th>\n",
       "      <th>signup_app</th>\n",
       "      <th>first_device_type</th>\n",
       "      <th>first_browser</th>\n",
       "      <th>secs_elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>20140101000936</td>\n",
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       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3427529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>20140101001558</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>207842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>20140101001639</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>1135444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>20140101002146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3755100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>20140101002619</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2555.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country_destination  timestamp_first_active  date_first_booking  gender  \\\n",
       "0                   11          20140101000936                   1       2   \n",
       "1                    7          20140101001558                   0       0   \n",
       "2                    7          20140101001639                   0       0   \n",
       "3                    7          20140101002146                   0       0   \n",
       "4                    5          20140101002619                   1       0   \n",
       "\n",
       "         age  signup_method  signup_flow  language  affiliate_channel  \\\n",
       "0  62.000000              0            0         5                  6   \n",
       "1  36.234347              0            0         5                  2   \n",
       "2  36.234347              0            0         5                  5   \n",
       "3  36.234347              0            0         5                  2   \n",
       "4  36.234347              0           25         5                  2   \n",
       "\n",
       "   affiliate_provider  first_affiliate_tracked  signup_app  first_device_type  \\\n",
       "0                   8                        3           2                  6   \n",
       "1                   4                        6           2                  3   \n",
       "2                   8                        3           2                  6   \n",
       "3                   4                        0           2                  6   \n",
       "4                   4                        6           3                  8   \n",
       "\n",
       "   first_browser  secs_elapsed  \n",
       "0              6     3427529.0  \n",
       "1             10      207842.0  \n",
       "2             10     1135444.0  \n",
       "3              6     3755100.0  \n",
       "4              0        2555.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import seaborn as sns\n",
    "#get correlations of each features in dataset\n",
    "corrmat = df_train.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,10))\n",
    "#plot heat map\n",
    "g=sns.heatmap(df_train[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0924696  0.62316372 0.01993862 0.07595927 0.00588022 0.00439656\n",
      " 0.00540426 0.01039747 0.00597833 0.0120829  0.00320881 0.01311816\n",
      " 0.01984893 0.10815314]\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Index' object has no attribute 'plot'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-71-07ac558e2fb7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#plot graph of feature importances for better visualization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mfeat_importances\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mfeat_importances\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'olive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Index' object has no attribute 'plot'"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "model = ExtraTreesClassifier()\n",
    "model.fit(x_train,y_train)\n",
    "print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers\n",
    "#plot graph of feature importances for better visualization\n",
    "feat_importances=x_train.columns\n",
    "feat_importances.plot(kind='bar',color='olive')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(sessions.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6112110820629477\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sanjana/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import time\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics\n",
    "\n",
    "lm = linear_model.LogisticRegression(multi_class='multinomial',solver='saga')\n",
    "\n",
    "lm.fit(x_train, y_train)\n",
    "lm.score(x_test, y_test)\n",
    "y_pred = lm.predict(x_test)\n",
    "\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44178 29453 44178 29453\n",
      "Log Reg= 0.8816758904016568\n",
      "Baseline: 88.29% (0.45%)\n",
      "Dec Tree= 0.8793331748888059\n",
      "Baseline: 88.01% (0.70%)\n"
     ]
    }
   ],
   "source": [
    "new_train= df_train[['country_destination','date_first_booking', 'age', 'gender', 'language', 'timestamp_first_active', ]].copy()\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X2 = new_train.iloc[:,1:8]\n",
    "Y2 = new_train.iloc[:,0]\n",
    "x_train2, x_test2, y_train2, y_test2 = train_test_split(X2, Y2, test_size = 0.4, random_state = 0)\n",
    "\n",
    "print(len(x_train2),len(x_test2),len(y_train2),len(y_test2))\n",
    "\n",
    "lm2 = linear_model.LogisticRegression(multi_class='ovr', solver='liblinear')\n",
    "lm2.fit(x_train2, y_train2)\n",
    "y_pred_lm = lm2.predict(x_test2)\n",
    "print(\"Log Reg=\",metrics.accuracy_score(y_test2, y_pred_lm))\n",
    "\n",
    "kfold = KFold(n_splits=10, shuffle=True)\n",
    "results = cross_val_score(lm2, x_train2, y_train2, cv=kfold)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "\n",
    "clf2 = DecisionTreeClassifier()\n",
    "clf2 = clf2.fit(x_train2,y_train2)\n",
    "y_pred_dt = clf2.predict(x_test2)\n",
    "print(\"Dec Tree=\",metrics.accuracy_score(y_test2, y_pred_dt))\n",
    "\n",
    "kfold = KFold(n_splits=20, shuffle=True)\n",
    "results = cross_val_score(clf2, x_train2, y_train2, cv=kfold)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>country_destination</th>\n",
       "      <th>date_first_booking</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>language</th>\n",
       "      <th>secs_elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3427529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>207842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1135444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3755100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>36.234347</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2555.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country_destination  date_first_booking        age  gender  language  \\\n",
       "0                   11                   1  62.000000       2         5   \n",
       "1                    7                   0  36.234347       0         5   \n",
       "2                    7                   0  36.234347       0         5   \n",
       "3                    7                   0  36.234347       0         5   \n",
       "4                    5                   1  36.234347       0         5   \n",
       "\n",
       "   secs_elapsed  \n",
       "0     3427529.0  \n",
       "1      207842.0  \n",
       "2     1135444.0  \n",
       "3     3755100.0  \n",
       "4        2555.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN =  0.597018979390894\n",
      "Baseline: 59.37% (0.90%)\n",
      "Random For= 0.8221233830170102\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nkfold = KFold(n_splits=10, shuffle=True)\\nresults = cross_val_score(classifier, x_train2, y_train2, cv=kfold)\\nprint(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\\n'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=15)\n",
    "knn.fit(x_train2, y_train2)\n",
    "y_pred_knn = knn.predict(x_test2)\n",
    "print(\"KNN = \",metrics.accuracy_score(y_test2, y_pred_knn))\n",
    "\n",
    "kfold = KFold(n_splits=20, shuffle=True)\n",
    "results = cross_val_score(knn, x_train2, y_train2, cv=kfold)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report    \n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "classifier= RandomForestClassifier(n_estimators= 250, criterion=\"entropy\")\n",
    "classifier.fit(x_train2, y_train2)\n",
    "y_pred_rf= classifier.predict(x_test2)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "#print(confusion_matrix(y_test2,y_pred_rf))\n",
    "#print(classification_report(y_test2,y_pred_rf))\n",
    "print(\"Random For=\",accuracy_score(y_test2, y_pred_rf))\n",
    "'''\n",
    "kfold = KFold(n_splits=10, shuffle=True)\n",
    "results = cross_val_score(classifier, x_train2, y_train2, cv=kfold)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 selected features\n",
      "['date_first_booking', 'age', 'gender', 'language', 'secs_elapsed']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "rfe_selector = RFE(estimator=DecisionTreeClassifier(), n_features_to_select=5, step=10, verbose=5)\n",
    "rfe_selector.fit(x_train2, y_train2)\n",
    "rfe_support = rfe_selector.get_support()\n",
    "rfe_feature = x_train2.loc[:,rfe_support].columns.tolist()\n",
    "print(str(len(rfe_feature)), 'selected features')\n",
    "print(rfe_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg acc= 0.6147421315315927\n",
      "voting= 0.6125012732149526\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sanjana/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline: 61.08% (0.67%)\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.metrics import accuracy_score, f1_score, log_loss\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier, ExtraTreesClassifier\n",
    "LogReg_clf = LogisticRegression()\n",
    "DTree_clf = DecisionTreeClassifier()\n",
    "SVC_clf = SVC()\n",
    "LogReg_clf.fit(x_train2, y_train2)\n",
    "DTree_clf.fit(x_train2, y_train2)\n",
    "SVC_clf.fit(x_train2, y_train2)\n",
    "\n",
    "LogReg_pred = LogReg_clf.predict(x_test2)\n",
    "DTree_pred = DTree_clf.predict(x_test2)\n",
    "SVC_pred = SVC_clf.predict(x_test2)\n",
    "\n",
    "averaged_preds = (LogReg_pred + DTree_pred + SVC_pred)//3\n",
    "acc = accuracy_score(y_test2, averaged_preds)\n",
    "print(\"Avg acc=\" ,acc)\n",
    "\n",
    "voting_clf = VotingClassifier(estimators=[('SVC', SVC_clf), ('DTree', DTree_clf), ('LogReg', LogReg_clf)], voting='hard')\n",
    "voting_clf.fit(x_train2, y_train2)\n",
    "preds = voting_clf.predict(x_test2)\n",
    "acc = accuracy_score(y_test2, preds)\n",
    "\n",
    "print(\"voting= \" + str(acc))\n",
    "\n",
    "kfold = KFold(n_splits=10, shuffle=True)\n",
    "results = cross_val_score(voting_clf, x_train2, y_train2, cv=kfold)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SVC_clf1 = SVC(kernel='linear')\n",
    "SVC_clf1.fit(x_train2, y_train2)\n",
    "SVC_pred1 = SVC_clf1.predict(x_test2)\n",
    "\n",
    "acc2 = accuracy_score(y_test2, SVC_pred1)\n",
    "print(acc2)\n",
    "\n",
    "kfold = KFold(n_splits=10, shuffle=True)\n",
    "results = cross_val_score(SVC_clf1, x_train2, y_train2, cv=kfold)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NV= 0.8765151257936373\n",
      "GB= 0.6112110820629477\n",
      "Baseline: 88.08% (0.51%)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# training a Naive Bayes classifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB().fit(x_train2, y_train2)\n",
    "gnb_predictions = gnb.predict(x_test2)\n",
    "# accuracy on X_test\n",
    "accuracy = gnb.score(x_test2, y_test2)\n",
    "print(\"NV=\",accuracy)\n",
    "# creating a confusion matrix\n",
    "cm = confusion_matrix(y_test, gnb_predictions)\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "gradient_booster = GradientBoostingClassifier(learning_rate=0.1)\n",
    "gradient_booster.get_params()\n",
    "gradient_booster.fit(x_train2,y_train2)\n",
    "print(\"GB=\",accuracy_score(y_test2, y_pred))\n",
    "\n",
    "kfold = KFold(n_splits=10, shuffle=True)\n",
    "results = cross_val_score(classifier, x_train2, y_train2, cv=kfold)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 61668 entries, 0 to 61667\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       61668 non-null  object \n",
      " 1   date_account_created     61668 non-null  object \n",
      " 2   timestamp_first_active   61668 non-null  int64  \n",
      " 3   date_first_booking       0 non-null      float64\n",
      " 4   gender                   61668 non-null  object \n",
      " 5   age                      33067 non-null  float64\n",
      " 6   signup_method            61668 non-null  object \n",
      " 7   signup_flow              61668 non-null  int64  \n",
      " 8   language                 61668 non-null  object \n",
      " 9   affiliate_channel        61668 non-null  object \n",
      " 10  affiliate_provider       61668 non-null  object \n",
      " 11  first_affiliate_tracked  61662 non-null  object \n",
      " 12  signup_app               61668 non-null  object \n",
      " 13  first_device_type        61668 non-null  object \n",
      " 14  first_browser            61668 non-null  object \n",
      " 15  secs_elapsed             61668 non-null  float64\n",
      "dtypes: float64(3), int64(2), object(11)\n",
      "memory usage: 8.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_test = pd.read_csv('/home/sanjana/Desktop/datasets/airbnb-recruiting-new-user-bookings/test_users.csv')\n",
    "\n",
    "df_test = pd.merge(df_test, sessions, how='inner', on = 'id')\n",
    "\n",
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                             0\n",
       "date_account_created           0\n",
       "timestamp_first_active         0\n",
       "date_first_booking         61668\n",
       "gender                         0\n",
       "age                        28601\n",
       "signup_method                  0\n",
       "signup_flow                    0\n",
       "language                       0\n",
       "affiliate_channel              0\n",
       "affiliate_provider             0\n",
       "first_affiliate_tracked        6\n",
       "signup_app                     0\n",
       "first_device_type              0\n",
       "first_browser                  0\n",
       "secs_elapsed                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: date_first_booking, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.drop(df_test[df_test['age'] > 120].index, inplace = True)\n",
    "df_test.drop(df_test[df_test['age'] < 18].index, inplace = True)\n",
    "df_test['age'].describe()\n",
    "df_test['age'].fillna(df_test['age'].mean(),inplace = True)\n",
    "\n",
    "\n",
    "df_test['date_first_booking'] = df_test['date_first_booking'].apply(lambda x: 1 if not pd.isnull(x) else 0)\n",
    "df_test['age'].fillna(df_test['age'].mean(),inplace = True)\n",
    "\n",
    "df_test.isnull().sum()\n",
    "\n",
    "df_test['date_first_booking'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'new_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-b117b7ca378b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'new_test' is not defined"
     ]
    }
   ],
   "source": [
    "new_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date_account_created</th>\n",
       "      <th>timestamp_first_active</th>\n",
       "      <th>date_first_booking</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>signup_method</th>\n",
       "      <th>signup_flow</th>\n",
       "      <th>language</th>\n",
       "      <th>affiliate_channel</th>\n",
       "      <th>affiliate_provider</th>\n",
       "      <th>first_affiliate_tracked</th>\n",
       "      <th>signup_app</th>\n",
       "      <th>first_device_type</th>\n",
       "      <th>first_browser</th>\n",
       "      <th>secs_elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5uwns89zht</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>20140701000006</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Moweb</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>Mobile Safari</td>\n",
       "      <td>119187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>jtl0dijy2j</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>20140701000051</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.831464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Moweb</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>Mobile Safari</td>\n",
       "      <td>250119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>xx0ulgorjt</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>20140701000148</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.831464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>linked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>975575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6c6puo6ix0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>20140701000215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.831464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>linked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>IE</td>\n",
       "      <td>123001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>czqhjk3yfe</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>20140701000305</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.831464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Safari</td>\n",
       "      <td>454023.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id date_account_created  timestamp_first_active  \\\n",
       "0  5uwns89zht           2014-07-01          20140701000006   \n",
       "1  jtl0dijy2j           2014-07-01          20140701000051   \n",
       "2  xx0ulgorjt           2014-07-01          20140701000148   \n",
       "3  6c6puo6ix0           2014-07-01          20140701000215   \n",
       "4  czqhjk3yfe           2014-07-01          20140701000305   \n",
       "\n",
       "   date_first_booking  gender        age  signup_method  signup_flow  \\\n",
       "0                   0       1  35.000000              1            0   \n",
       "1                   0       0  34.831464              0            0   \n",
       "2                   0       0  34.831464              0            0   \n",
       "3                   0       0  34.831464              0            0   \n",
       "4                   0       0  34.831464              0            0   \n",
       "\n",
       "   language affiliate_channel affiliate_provider first_affiliate_tracked  \\\n",
       "0         6            direct             direct               untracked   \n",
       "1         6            direct             direct               untracked   \n",
       "2         6            direct             direct                  linked   \n",
       "3         6            direct             direct                  linked   \n",
       "4         6            direct             direct               untracked   \n",
       "\n",
       "  signup_app first_device_type  first_browser  secs_elapsed  \n",
       "0      Moweb            iPhone  Mobile Safari      119187.0  \n",
       "1      Moweb            iPhone  Mobile Safari      250119.0  \n",
       "2        Web   Windows Desktop         Chrome      975575.0  \n",
       "3        Web   Windows Desktop             IE      123001.0  \n",
       "4        Web       Mac Desktop         Safari      454023.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "gender=LabelEncoder()\n",
    "df_test['gender']=gender.fit_transform(df_test['gender'])\n",
    "language=LabelEncoder()\n",
    "df_test['language']=language.fit_transform(df_test['language'])\n",
    "signup_method=LabelEncoder()\n",
    "df_test['signup_method']=signup_method.fit_transform(df_test['signup_method'])\n",
    "df_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>signup_method</th>\n",
       "      <th>date_first_booking</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>language</th>\n",
       "      <th>secs_elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>119187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.831464</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>250119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.831464</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>975575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.831464</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>123001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.831464</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>454023.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61663</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2534232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61664</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.831464</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>51618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61665</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.831464</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>823297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61666</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.831464</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>352068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61667</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2804544.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61591 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       signup_method  date_first_booking        age  gender  language  \\\n",
       "0                  1                   0  35.000000       1         6   \n",
       "1                  0                   0  34.831464       0         6   \n",
       "2                  0                   0  34.831464       0         6   \n",
       "3                  0                   0  34.831464       0         6   \n",
       "4                  0                   0  34.831464       0         6   \n",
       "...              ...                 ...        ...     ...       ...   \n",
       "61663              0                   0  31.000000       0         6   \n",
       "61664              0                   0  34.831464       0        14   \n",
       "61665              0                   0  34.831464       0         4   \n",
       "61666              0                   0  34.831464       0         6   \n",
       "61667              0                   0  49.000000       1         6   \n",
       "\n",
       "       secs_elapsed  \n",
       "0          119187.0  \n",
       "1          250119.0  \n",
       "2          975575.0  \n",
       "3          123001.0  \n",
       "4          454023.0  \n",
       "...             ...  \n",
       "61663     2534232.0  \n",
       "61664       51618.0  \n",
       "61665      823297.0  \n",
       "61666      352068.0  \n",
       "61667     2804544.0  \n",
       "\n",
       "[61591 rows x 6 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_test= df_test[['signup_method','date_first_booking', 'age','gender',\n",
    "                      'language', 'secs_elapsed']].copy()\n",
    "new_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "X has 6 features per sample; expecting 5",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-956f6319962c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0my_pred_sub\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlm2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_TEST\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mnew_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'country_destination'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_pred_sub\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    307\u001b[0m             \u001b[0mPredicted\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0mper\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m         \"\"\"\n\u001b[0;32m--> 309\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    310\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36mdecision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    287\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mn_features\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m             raise ValueError(\"X has %d features per sample; expecting %d\"\n\u001b[0;32m--> 289\u001b[0;31m                              % (X.shape[1], n_features))\n\u001b[0m\u001b[1;32m    290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m         scores = safe_sparse_dot(X, self.coef_.T,\n",
      "\u001b[0;31mValueError\u001b[0m: X has 6 features per sample; expecting 5"
     ]
    }
   ],
   "source": [
    "X_TEST = new_train.iloc[:,0:17]\n",
    "\n",
    "\n",
    "y_pred_sub = lm2.predict(X_TEST)\n",
    "\n",
    "new_test['country_destination'] = y_pred_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>zp8xfonng8</td>\n",
       "      <td>NDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62093</th>\n",
       "      <td>fa6260ziny</td>\n",
       "      <td>NDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62094</th>\n",
       "      <td>87k0fy4ugm</td>\n",
       "      <td>NDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62095</th>\n",
       "      <td>9uqfg8txu3</td>\n",
       "      <td>NDF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62096 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id country\n",
       "0      5uwns89zht     NDF\n",
       "1      jtl0dijy2j     NDF\n",
       "2      xx0ulgorjt     NDF\n",
       "3      6c6puo6ix0     NDF\n",
       "4      czqhjk3yfe     NDF\n",
       "...           ...     ...\n",
       "62091  cv0na2lf5a     NDF\n",
       "62092  zp8xfonng8     NDF\n",
       "62093  fa6260ziny     NDF\n",
       "62094  87k0fy4ugm     NDF\n",
       "62095  9uqfg8txu3     NDF\n",
       "\n",
       "[62096 rows x 2 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = pd.read_csv('/home/sanjana/Desktop/datasets/airbnb-recruiting-new-user-bookings/sample_submission_NDF.csv')\n",
    "sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df=sub_df.drop(['Survived'], axis = 1) \n",
    "sub_df['Survived']=y_pred\n",
    "print(sub_df)\n",
    "sub_df.to_csv('final_sub3.csv')"
   ]
  }
 ],
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