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FLIGHT DELAY PREDICTION BASED ON AVIATION

Gauri D. Vedpathak , Shubhangi M. Vitalkar

Gauri D. Vedpathak MCA & Trinity Academy Of Engineering ,Pune

Shubhangi M. Vitalkar MCA & Trinity Academy Of Engineering ,Pune

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ABSTRACT - Flight delays have become a significant concern in the aviation industry, impacting passengers, airlines, and airport operations. This research paper presents a machine learning-based approach to predict flight delays using historical aviation data. The study focuses on analyzing key factors such as weather conditions, airline schedules, airport traffic, and departure/arrival times to identify patterns that contribute to delays. Various classification algorithms, including Random Forest, Logistic Regression, and Decision Trees, are applied and compared based on performance metrics like accuracy, precision, and recall. The model is trained on real-world aviation datasets, preprocessed to handle missing values and categorical features. The results indicate that the proposed model can effectively predict delays, aiding stakeholders in proactive planning and decision-making. This research contributes to enhancing operational efficiency in aviation and improving the passenger experience through timely and reliable predictions.

Key Words: flight delay, aviation, machine,learning,  prediction historical data, weather conditions.