Data Mining Project
This project aims to predict flight delays (exceeding 15 minutes) or cancellations using classification algorithms. The analysis leverages flight data encompassing scheduling, airline carriers, airports, and flight distance. The core of this project is to build a predictive model that can identify flights at high risk of being delayed or canceled. By utilizing machine learning classification techniques on a comprehensive flight dataset, we aim to provide insights that can be beneficial for various stakeholders in the aviation industry.
- Improved Passenger Experience: Passengers can make more informed decisions by choosing lower-risk flights, leading to a smoother travel experience.
- Airline Operational Optimization: Airlines can optimize their operations, including crew scheduling, aircraft allocation, and resource management, to mitigate the impact of disruptions.
- Cost Reduction: Minimizing delays and cancellations can significantly reduce operational costs for airlines, such as compensation, rebooking expenses, and maintenance.
- Enhanced Air Traffic Management: Better prediction capabilities can assist air traffic control in managing air traffic flow more effectively, reducing congestion and improving overall efficiency.
The project utilizes the "2015 Flight Delays and Cancellations" dataset, specifically the flights.csv file. This dataset contains approximately 5.8 million flight records with various numerical and categorical features (no textual data).
Dataset Source: [flight delays]