Overview

Data

The dataset for predicting customer buying behavior contains a history of customer booking. There are 50,000 bookings with 14 columns. Below is the detailed description of each column:

• num_passengers = number of passengers travelling
• sales_channel = sales channel booking was made on
• trip_type = trip Type (Round Trip, One Way, Circle Trip)
• purchase_lead = number of days between travel date and booking date
• length_of_stay = number of days spent at destination
• flight_hour = hour of flight departure
• flight_day = day of week of flight departure
• route = origin -> destination flight route
• booking_origin = country from where booking was made
• wants_extra_baggage = if the customer wanted extra baggage in the booking
• wants_preferred_seat = if the customer wanted a preferred seat in the booking
• wants_in_flight_meals = if the customer wanted in-flight meals in the booking
• flight_duration = total duration of flight (in hours)
• booking_complete = flag indicating if the customer completed the booking

Objectives

• Build the model to predict customer buying behavior
• Find the contribution of each variable

Result

Model

Model with input of 13 features

With Random Forest as the model and precision as the metric, the precision score is 0,713.

Feature Contribution

Below, is the contribution of each variable to customer buying behavior:

Model with input of 5 features

Meanwhile, model with 5 high contribution has lower precision score, it's 0.6993

Analysis of AUC on both models

Model with 13 input features has higher AUC (AUC = 0.79) than model with 5 input features (AUC = 0.77). Means that Model 13 input features can easily distinguishes between classes (which behavior lead to booking completion or not).

Customer buying behavior is highly affected by the following:

• How long did the customer book the plane before the travel
• The duration, the route, and the origin of the customer
• and how long the customer will stay at the destination