The task used real flight and booking data rather than a purely theoretical exercise, which made it feel close to how BA's Airport Planning and Data Science teams actually work — from building an eligibility model with limited data to training a classification model and presenting results to a non-technical stakeholder.

I liked both projects because they were based on real business problems. Project 1: I enjoyed working with flight data and understanding how data can help plan lounge demand. Project 2: I enjoyed learning how to prepare customer data and use Machine Learning to predict customer buying behaviour. Overall, both projects helped me understand how data can help businesses make better decisions.

I liked that the simulation was practical and realistic, because it allowed me to apply data analysis and machine-learning concepts to a real-world customer booking problem. I also liked that it helped me understand how different variables can influence booking decisions and how the model can support better business decisions.