This project is part of the British Airways Data Science Job Simulation offered via Forage.
The goal was to step into the role of a Data Scientist and solve two real-world challenges:
- Modeling Lounge Eligibility at Heathrow Terminal 3 to support future capacity planning.
- Predicting Customer Buying Behaviour to help the marketing team target future holiday travelers.
Through data modeling, machine learning, and visualization, the project provides actionable insights for operational efficiency and customer acquisition strategy.
The dataset provided in the simulation contains passenger information, booking details, and customer travel history.
Sensitive data has been anonymized, but real-world data structures are maintained to replicate an authentic analytics environment.
- Python 3
- Pandas – data cleaning and manipulation
- NumPy – numerical computations
- Matplotlib – data visualization
- Scikit-learn – machine learning modeling and evaluation
- Jupyter Notebook – analysis and documentation
- Prepared and transformed dataset for machine learning.
- Trained predictive models to classify potential future holiday travelers.
- Evaluated model performance and selected the most reliable prediction approach.
- Built a framework to forecast lounge demand using passenger segmentation.
- Identified key customer attributes influencing holiday travel bookings.
- Delivered data-driven strategies for capacity planning and marketing outreach.
- Clone this repository
git clone https://github.com/yourusername/aerovision.git