This project is intended to help Ryanir company to build a Predictor ML Model for their food selling options during flight time. The main goal is to predict with accuracy the quantity of each product, so food waste would be as small as possible.
For this problem, Random Forest Regressor was used, as it is a robust ML Model, having an evaluation score of MSE: 1.4
- Preferred Products: Ham-cheese paninis emerge as the favored choice among passengers for in-flight catering.
- Flight Duration Impact: There is an increased demand for food, particularly fresh options, during long fligths that exceeds 2.5 hours.
- Time Sensitivity: Flight delays impact the demand for in-flight food. Additionally, the first and last hour of flights witness a surge in demand for fresh food items.