task2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2\n",
    "\n",
    "---\n",
    "\n",
    "## Predictive modeling of customer bookings\n",
    "\n",
    "This Jupyter notebook includes some code to get you started with this predictive modeling task. We will use various packages for data manipulation, feature engineering and machine learning.\n",
    "\n",
    "### Exploratory data analysis\n",
    "\n",
    "First, we must explore the data in order to better understand what we have and the statistical properties of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/customer_booking.csv\", encoding=\"ISO-8859-1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.head()` method allows us to view the first 5 rows in the dataset, this is useful for visual inspection of our columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": "markdown",
   "metadata": {},
   "source": [
    "The `.info()` method gives us a data description, telling us the names of the columns, their data types and how many null values we have. Fortunately, we have no null values. It looks like some of these columns should be converted into different data types, e.g. flight_day.\n",
    "\n",
    "To provide more context, below is a more detailed data description, explaining exactly what each column means:\n",
    "\n",
    "- `num_passengers` = number of passengers travelling\n",
    "- `sales_channel` = sales channel booking was made on\n",
    "- `trip_type` = trip Type (Round Trip, One Way, Circle Trip)\n",
    "- `purchase_lead` = number of days between travel date and booking date\n",
    "- `length_of_stay` = number of days spent at destination\n",
    "- `flight_hour` = hour of flight departure\n",
    "- `flight_day` = day of week of flight departure\n",
    "- `route` = origin -> destination flight route\n",
    "- `booking_origin` = country from where booking was made\n",
    "- `wants_extra_baggage` = if the customer wanted extra baggage in the booking\n",
    "- `wants_preferred_seat` = if the customer wanted a preferred seat in the booking\n",
    "- `wants_in_flight_meals` = if the customer wanted in-flight meals in the booking\n",
    "- `flight_duration` = total duration of flight (in hours)\n",
    "- `booking_complete` = flag indicating if the customer completed the booking\n",
    "\n",
    "Before we compute any statistics on the data, lets do any necessary data conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sat', 'Wed', 'Thu', 'Mon', 'Sun', 'Tue', 'Fri'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"flight_day\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 3, 4, 1, 7, 2, 5], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"flight_day\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</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",
       "      <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>3.814420</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>1.992792</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>1.000000</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>2.000000</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>4.000000</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>5.000000</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>7.000000</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",
       "         flight_day  wants_extra_baggage  wants_preferred_seat  \\\n",
       "count  50000.000000         50000.000000          50000.000000   \n",
       "mean       3.814420             0.668780              0.296960   \n",
       "std        1.992792             0.470657              0.456923   \n",
       "min        1.000000             0.000000              0.000000   \n",
       "25%        2.000000             0.000000              0.000000   \n",
       "50%        4.000000             1.000000              0.000000   \n",
       "75%        5.000000             1.000000              1.000000   \n",
       "max        7.000000             1.000000              1.000000   \n",
       "\n",
       "       wants_in_flight_meals  flight_duration  booking_complete  \n",
       "count           50000.000000     50000.000000      50000.000000  \n",
       "mean                0.427140         7.277561          0.149560  \n",
       "std                 0.494668         1.496863          0.356643  \n",
       "min                 0.000000         4.670000          0.000000  \n",
       "25%                 0.000000         5.620000          0.000000  \n",
       "50%                 0.000000         7.570000          0.000000  \n",
       "75%                 1.000000         8.830000          0.000000  \n",
       "max                 1.000000         9.500000          1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.describe()` method gives us a summary of descriptive statistics over the entire dataset (only works for numeric columns). This gives us a quick overview of a few things such as the mean, min, max and overall distribution of each column.\n",
    "\n",
    "From this point, you should continue exploring the dataset with some visualisations and other metrics that you think may be useful. Then, you should prepare your dataset for predictive modelling. Finally, you should train your machine learning model, evaluate it with performance metrics and output visualisations for the contributing variables. All of this analysis should be summarised in your single slide."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exploring Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_passengers             int64\n",
       "sales_channel             object\n",
       "trip_type                 object\n",
       "purchase_lead              int64\n",
       "length_of_stay             int64\n",
       "flight_hour                int64\n",
       "flight_day                 int64\n",
       "route                     object\n",
       "booking_origin            object\n",
       "wants_extra_baggage        int64\n",
       "wants_preferred_seat       int64\n",
       "wants_in_flight_meals      int64\n",
       "flight_duration          float64\n",
       "booking_complete           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 14)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df.drop('booking_complete',axis=1)\n",
    "y= df.booking_complete         \n",
    "\n",
    "#changing object dtype to int dtype\n",
    "for colname in X.select_dtypes(\"object\"):\n",
    "    X[colname], _ = X[colname].factorize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_passengers             int64\n",
       "sales_channel              int64\n",
       "trip_type                  int64\n",
       "purchase_lead              int64\n",
       "length_of_stay             int64\n",
       "flight_hour                int64\n",
       "flight_day                 int64\n",
       "route                      int64\n",
       "booking_origin             int64\n",
       "wants_extra_baggage        int64\n",
       "wants_preferred_seat       int64\n",
       "wants_in_flight_meals      int64\n",
       "flight_duration          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "route                    0.051077\n",
       "booking_origin           0.047768\n",
       "flight_duration          0.020523\n",
       "wants_extra_baggage      0.011918\n",
       "length_of_stay           0.008276\n",
       "num_passengers           0.004546\n",
       "wants_in_flight_meals    0.002494\n",
       "trip_type                0.002408\n",
       "purchase_lead            0.002399\n",
       "sales_channel            0.002357\n",
       "wants_preferred_seat     0.001375\n",
       "flight_day               0.000317\n",
       "flight_hour              0.000000\n",
       "Name: MI Scores, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "mi_scores = mutual_info_classif(X, y)\n",
    "mi_scores = pd.Series(mi_scores, name=\"MI Scores\", index=X.columns)\n",
    "mi_scores = mi_scores.sort_values(ascending=False)\n",
    "\n",
    "mi_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_mi_scores(scores):\n",
    "    scores = scores.sort_values(ascending=True)\n",
    "    width = np.arange(len(scores))\n",
    "    ticks = list(scores.index)\n",
    "    plt.barh(width, scores)\n",
    "    plt.yticks(width, ticks)\n",
    "    plt.title(\"Mutual Information Scores\")\n",
    "\n",
    "\n",
    "plt.figure(dpi=100, figsize=(8, 5))\n",
    "plot_mi_scores(mi_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test train data split\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def dataset(X,y):\n",
    "    train_full_X, val_X, train_full_y, val_y = train_test_split(X, y, test_size = 0.2, random_state =0)\n",
    "    \n",
    "    train_X, test_X, train_y, val_y = train_test_split(train_full_X, train_full_y, test_size = 0.25, random_state = 0)\n",
    "    return(train_X, val_X, train_y, val_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "def scale(X):\n",
    "    scaler = MinMaxScaler()\n",
    "    scaler.fit(X)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 1: Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY:  80.08\n",
      "AUC SCORE:  0.5015899814756185\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "features = ['route', 'booking_origin', 'flight_duration','wants_extra_baggage', 'length_of_stay', 'num_passengers' ]\n",
    "X = df[features]\n",
    "\n",
    "X = pd.get_dummies(X, columns=features)\n",
    "X = scale(X)\n",
    "y = df.booking_complete\n",
    "\n",
    "X_train, X_val, y_train, y_val = dataset(X,y)\n",
    "\n",
    "forest_model = RandomForestClassifier(random_state=1)\n",
    "forest_model.fit(X_train, y_train)\n",
    "preds = forest_model.predict(X_val)\n",
    "\n",
    "print('ACCURACY: ', accuracy_score(y_val, preds)*100)\n",
    "print('AUC SCORE: ', roc_auc_score(y_val, preds))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY:  82.16\n",
      "AUC score:  0.5002407834360953\n"
     ]
    }
   ],
   "source": [
    "X= df.drop('booking_complete',axis=1)\n",
    "#one hot encoding\n",
    "X = pd.get_dummies(X)\n",
    "X= scale(X)\n",
    "y= df.booking_complete       \n",
    "\n",
    "X_train,X_val,y_train,y_val= dataset(X,y)\n",
    "\n",
    "forest_model= RandomForestClassifier(random_state=1)\n",
    "forest_model.fit(X_train, y_train)\n",
    "preds= forest_model.predict(X_val)\n",
    "\n",
    "print('ACCURACY: ',accuracy_score(y_val,preds)*100)\n",
    "print('AUC score: ',roc_auc_score(y_val,preds))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model XGB Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Downloading xgboost-1.7.5-py3-none-win_amd64.whl (70.9 MB)\n",
      "     -------------------------------------- 70.9/70.9 MB 632.9 kB/s eta 0:00:00\n",
      "Requirement already satisfied: scipy in c:\\users\\kudzai prichard\\anaconda3\\lib\\site-packages (from xgboost) (1.10.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\kudzai prichard\\anaconda3\\lib\\site-packages (from xgboost) (1.23.5)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-1.7.5\n",
      "^C\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Downloading xgboost-1.7.5-py3-none-win_amd64.whl (70.9 MB)\n",
      "     -------------------------------------- 70.9/70.9 MB 627.5 kB/s eta 0:00:00\n",
      "Requirement already satisfied: numpy in c:\\users\\kudzai prichard\\anaconda3\\lib\\site-packages (from xgboost) (1.23.5)\n",
      "Requirement already satisfied: scipy in c:\\users\\kudzai prichard\\anaconda3\\lib\\site-packages (from xgboost) (1.10.0)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-1.7.5\n"
     ]
    }
   ],
   "source": [
    "%pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY:  83.63000000000001\n",
      "AUC score:  0.5001948252571805\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "X= df[features]\n",
    "\n",
    "#one hot encoding\n",
    "X = pd.get_dummies(X, columns=features)\n",
    "X= scale(X)\n",
    "\n",
    "y= df.booking_complete    \n",
    "\n",
    "X_train,X_val,y_train,y_val= dataset(X,y)\n",
    "xgb_model = XGBClassifier()\n",
    "\n",
    "xgb_model.fit(X_train, y_train)\n",
    "prediction_xgb = xgb_model.predict(X_val)\n",
    "print('ACCURACY: ',accuracy_score(y_val, prediction_xgb)*100)\n",
    "print('AUC score: ',roc_auc_score(y_val,prediction_xgb))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY:  83.07\n",
      "AUC score:  0.5004325292224243\n"
     ]
    }
   ],
   "source": [
    "X= df.drop('booking_complete',axis=1)\n",
    "#one hot encoding\n",
    "X = pd.get_dummies(X)\n",
    "X= scale(X)\n",
    "y= df.booking_complete \n",
    "\n",
    "X_train,X_val,y_train,y_val= dataset(X,y)\n",
    "\n",
    "\n",
    "xgb_model = XGBClassifier()\n",
    "xgb_model.fit(X_train, y_train)\n",
    "prediction_xgb = xgb_model.predict(X_val)\n",
    "print('ACCURACY: ',accuracy_score(y_val, prediction_xgb)*100)\n",
    "print('AUC score: ',roc_auc_score(y_val,prediction_xgb))"
   ]
  }
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