Customer Analysis CB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get current working directory\n",
    "\n",
    "cwd = os.getcwd()\n",
    "\n",
    "#read the csv\n",
    "\n",
    "df = pd.read_csv(cwd + \"/customer_booking.csv\",  encoding=\"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>route</th>\n",
       "      <th>booking_origin</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>262</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>112</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>243</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>96</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>68</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
       "0               2      Internet  RoundTrip            262              19   \n",
       "1               1      Internet  RoundTrip            112              20   \n",
       "2               2      Internet  RoundTrip            243              22   \n",
       "3               1      Internet  RoundTrip             96              31   \n",
       "4               2      Internet  RoundTrip             68              22   \n",
       "\n",
       "   flight_hour flight_day   route booking_origin  wants_extra_baggage  \\\n",
       "0            7        Sat  AKLDEL    New Zealand                    1   \n",
       "1            3        Sat  AKLDEL    New Zealand                    0   \n",
       "2           17        Wed  AKLDEL          India                    1   \n",
       "3            4        Sat  AKLDEL    New Zealand                    0   \n",
       "4           15        Wed  AKLDEL          India                    1   \n",
       "\n",
       "   wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
       "0                     0                      0             5.52   \n",
       "1                     0                      0             5.52   \n",
       "2                     1                      0             5.52   \n",
       "3                     0                      1             5.52   \n",
       "4                     0                      1             5.52   \n",
       "\n",
       "   booking_complete  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 14)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>num_passengers</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.591240</td>\n",
       "      <td>84.940480</td>\n",
       "      <td>23.04456</td>\n",
       "      <td>9.06634</td>\n",
       "      <td>0.668780</td>\n",
       "      <td>0.296960</td>\n",
       "      <td>0.427140</td>\n",
       "      <td>7.277561</td>\n",
       "      <td>0.149560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.020165</td>\n",
       "      <td>90.451378</td>\n",
       "      <td>33.88767</td>\n",
       "      <td>5.41266</td>\n",
       "      <td>0.470657</td>\n",
       "      <td>0.456923</td>\n",
       "      <td>0.494668</td>\n",
       "      <td>1.496863</td>\n",
       "      <td>0.356643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.670000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.620000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.570000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.830000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>867.000000</td>\n",
       "      <td>778.00000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers  purchase_lead  length_of_stay  flight_hour  \\\n",
       "count    50000.000000   50000.000000     50000.00000  50000.00000   \n",
       "mean         1.591240      84.940480        23.04456      9.06634   \n",
       "std          1.020165      90.451378        33.88767      5.41266   \n",
       "min          1.000000       0.000000         0.00000      0.00000   \n",
       "25%          1.000000      21.000000         5.00000      5.00000   \n",
       "50%          1.000000      51.000000        17.00000      9.00000   \n",
       "75%          2.000000     115.000000        28.00000     13.00000   \n",
       "max          9.000000     867.000000       778.00000     23.00000   \n",
       "\n",
       "       wants_extra_baggage  wants_preferred_seat  wants_in_flight_meals  \\\n",
       "count         50000.000000          50000.000000           50000.000000   \n",
       "mean              0.668780              0.296960               0.427140   \n",
       "std               0.470657              0.456923               0.494668   \n",
       "min               0.000000              0.000000               0.000000   \n",
       "25%               0.000000              0.000000               0.000000   \n",
       "50%               1.000000              0.000000               0.000000   \n",
       "75%               1.000000              1.000000               1.000000   \n",
       "max               1.000000              1.000000               1.000000   \n",
       "\n",
       "       flight_duration  booking_complete  \n",
       "count     50000.000000      50000.000000  \n",
       "mean          7.277561          0.149560  \n",
       "std           1.496863          0.356643  \n",
       "min           4.670000          0.000000  \n",
       "25%           5.620000          0.000000  \n",
       "50%           7.570000          0.000000  \n",
       "75%           8.830000          0.000000  \n",
       "max           9.500000          1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 14 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   num_passengers         50000 non-null  int64  \n",
      " 1   sales_channel          50000 non-null  object \n",
      " 2   trip_type              50000 non-null  object \n",
      " 3   purchase_lead          50000 non-null  int64  \n",
      " 4   length_of_stay         50000 non-null  int64  \n",
      " 5   flight_hour            50000 non-null  int64  \n",
      " 6   flight_day             50000 non-null  object \n",
      " 7   route                  50000 non-null  object \n",
      " 8   booking_origin         50000 non-null  object \n",
      " 9   wants_extra_baggage    50000 non-null  int64  \n",
      " 10  wants_preferred_seat   50000 non-null  int64  \n",
      " 11  wants_in_flight_meals  50000 non-null  int64  \n",
      " 12  flight_duration        50000 non-null  float64\n",
      " 13  booking_complete       50000 non-null  int64  \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_internet = df.sales_channel.value_counts().values[0]  / df.sales_channel.count() *100\n",
    "per_mobile = df.sales_channel.value_counts().values[1]  / df.sales_channel.count() *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of bookings done through internet: 88.764 %\n",
      "Number of bookings done through phone call: 11.236 %\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of bookings done through internet: {per_internet} %\")\n",
    "print(f\"Number of bookings done through phone call: {per_mobile} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_round = df.trip_type.value_counts().values[0]/ df.trip_type.count() *100\n",
    "per_oneway = df.trip_type.value_counts().values[1]/ df.trip_type.count() *100\n",
    "per_circle = df.trip_type.value_counts().values[2]/ df.trip_type.count() *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of round trips: 98.994 %\n",
      "Percentage of One way trips: 0.774 %\n",
      "Percentage of circle trips: 0.232 %\n"
     ]
    }
   ],
   "source": [
    "print(f\"Percentage of round trips: {per_round} %\")\n",
    "print(f\"Percentage of One way trips: {per_oneway} %\")\n",
    "print(f\"Percentage of circle trips: {per_circle} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='purchase_lead', ylabel='Count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.histplot(data=df, x=\"purchase_lead\", binwidth=30, kde=True, color='purple')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "purchase_lead\n",
       "False    49992\n",
       "True         8\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.purchase_lead >600).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>route</th>\n",
       "      <th>booking_origin</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>3</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>641</td>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "      <td>Sun</td>\n",
       "      <td>AKLKUL</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.83</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6148</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>614</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>Wed</td>\n",
       "      <td>COKMEL</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24119</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>704</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>Tue</td>\n",
       "      <td>PNHSYD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38356</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>633</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>Sat</td>\n",
       "      <td>HKTOOL</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39417</th>\n",
       "      <td>1</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>625</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>Fri</td>\n",
       "      <td>ICNRGN</td>\n",
       "      <td>Myanmar (Burma)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42916</th>\n",
       "      <td>1</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>605</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>Thu</td>\n",
       "      <td>BLRMEL</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46716</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>606</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Fri</td>\n",
       "      <td>HKTTPE</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48259</th>\n",
       "      <td>3</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>867</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>Mon</td>\n",
       "      <td>KIXMLE</td>\n",
       "      <td>Japan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
       "835                 3      Internet  RoundTrip            641              46   \n",
       "6148                1      Internet  RoundTrip            614              19   \n",
       "24119               1      Internet  RoundTrip            704              23   \n",
       "38356               2      Internet  RoundTrip            633               5   \n",
       "39417               1        Mobile  RoundTrip            625               5   \n",
       "42916               1        Mobile  RoundTrip            605               6   \n",
       "46716               2      Internet  RoundTrip            606               6   \n",
       "48259               3      Internet  RoundTrip            867               6   \n",
       "\n",
       "       flight_hour flight_day   route   booking_origin  wants_extra_baggage  \\\n",
       "835              6        Sun  AKLKUL         Malaysia                    1   \n",
       "6148            11        Wed  COKMEL        Australia                    0   \n",
       "24119            8        Tue  PNHSYD        Australia                    0   \n",
       "38356           10        Sat  HKTOOL        Australia                    0   \n",
       "39417           15        Fri  ICNRGN  Myanmar (Burma)                    0   \n",
       "42916           18        Thu  BLRMEL            India                    0   \n",
       "46716            6        Fri  HKTTPE    United States                    0   \n",
       "48259            7        Mon  KIXMLE            Japan                    0   \n",
       "\n",
       "       wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
       "835                       0                      1             8.83   \n",
       "6148                      0                      0             8.83   \n",
       "24119                     0                      0             8.58   \n",
       "38356                     0                      1             8.83   \n",
       "39417                     0                      0             6.62   \n",
       "42916                     0                      0             8.83   \n",
       "46716                     0                      1             4.67   \n",
       "48259                     0                      1             7.00   \n",
       "\n",
       "       booking_complete  \n",
       "835                   1  \n",
       "6148                  0  \n",
       "24119                 0  \n",
       "38356                 0  \n",
       "39417                 0  \n",
       "42916                 0  \n",
       "46716                 0  \n",
       "48259                 1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.purchase_lead > 600]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.purchase_lead <600 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='purchase_lead', ylabel='Count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.histplot(data=df, x=\"purchase_lead\", binwidth=30, kde=True, color='pink')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "length_of_stay\n",
       "False    49713\n",
       "True       279\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.length_of_stay> 200).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_complete\n",
       "0    9\n",
       "1    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.length_of_stay> 500].booking_complete.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.purchase_lead <500 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {\n",
    "    \"Mon\" : 1,\n",
    "    \"Tue\" : 2,\n",
    "    \"Wed\" : 3,\n",
    "    \"Thu\" : 4,\n",
    "    \"Fri\" : 5,\n",
    "    \"Sat\" : 6,\n",
    "    \"Sun\" : 7\n",
    "}\n",
    "\n",
    "df.flight_day = df.flight_day.map(mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flight_day\n",
       "1    8100\n",
       "3    7671\n",
       "2    7670\n",
       "4    7423\n",
       "5    6759\n",
       "7    6550\n",
       "6    5809\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.flight_day.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of bookings')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "ax = df.booking_origin.value_counts()[:20].plot(kind=\"bar\", color='lightgreen')\n",
    "ax.set_xlabel(\"Countries\")\n",
    "ax.set_ylabel(\"Number of bookings\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of complete bookings')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "ax = df[df.booking_complete ==1].booking_origin.value_counts()[:20].plot(kind=\"bar\", color='lightpink')\n",
    "ax.set_xlabel(\"Countries\")\n",
    "ax.set_ylabel(\"Number of complete bookings\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "successful_booking_per = df.booking_complete.value_counts().values[0] / len(df) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsuccessful_booking_per = 100-successful_booking_per"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of 50000 booking entries only 14.96 % bookings were successfull or complete.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Out of 50000 booking entries only {round(unsuccessful_booking_per,2)} % bookings were successfull or complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(cwd + \"/filtered_customer_booking.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}