British Airways – Customer Booking Prediction & Analytics
Predictive Modeling: Developed a Random Forest classifier using scikit-learn to predict flight booking completions (booking_complete) across 50,000 customer sessions. Feature Engineering: Extracted high-value commercial indicators including lead-time dynamics, route frequency encoding, and ancillary bundling behavior. Stakeholder Deliverables: Translated model feature importances into strategic recommendations and an executive slide deck to optimize direct booking conversions.
Overview
Developed an end-to-end Machine Learning solution to analyze customer booking behavior and predict flight booking completions using a dataset of 50,000 customer sessions. Built a Random Forest classification model using Scikit-learn to identify patterns influencing booking decisions and understand the factors affecting conversion rates. Performed feature engineering to extract meaningful commercial indicators, including purchase lead-time patterns, route frequency, and ancillary service bundling behavior. Evaluated feature importance to identify key factors influencing customer booking decisions. Translated model insights into actionable business recommendations to help British Airways improve direct booking conversions and optimize its digital booking strategy. Presented the findings through an executive presentation highlighting predictive insights and potential business improvements.
Problem
British Airways needed to better understand customer booking behavior and identify factors influencing whether customers complete their flight bookings. Without predictive insights, improving booking conversion rates and optimizing the booking experience becomes challenging.
Solution
Developed a Machine Learning classification model using Random Forest to predict flight booking completions. Analyzed customer session data, identified important booking patterns, and generated actionable business recommendations to improve direct booking conversions.
Features
- 01Flight booking completion prediction
- 02Customer booking behavior analysis
- 03Feature engineering and data preprocessing
- 04Route frequency and purchase lead-time analysis
- 05Feature importance analysis
- 06Data-driven business recommendations
Technologies
Development details
Developed an end-to-end predictive analytics solution using 50,000 customer booking sessions. Performed data cleaning, preprocessing, and exploratory data analysis to understand customer booking patterns. Applied feature engineering to extract important indicators such as purchase lead time, route frequency, and additional service selections. Trained a Random Forest classifier using Scikit-learn to predict the booking_complete outcome. Analyzed feature importance to identify key factors influencing booking decisions and translated the findings into strategic recommendations through an executive presentation.
Key learnings
- Understanding real-world customer booking behavior
- Performing data cleaning and exploratory data analysis
- Developing meaningful features through feature engineering
- Building and evaluating Random Forest classification models
- Understanding feature importance and model interpretation
- Converting machine learning insights into business recommendations
- Communicating technical findings through executive presentations