Application of Ml Algorithm for Flight Delay Forecasting

the airline sector has a serious problem with flight delays. The expansion of the aviation industry during the past two decades has increased air traffic, which has caused flight delays. Flight delays can create major irritation and financial costs for both carriers and passengers. Planes, airlines, and passengers can all benefit from accurate flight delay forecasting. To train and evaluate our machine learning model, we used a dataset that included aircraft information, weather data, and airport congestion data. As a result, by adopting certain actions, they try their best to prevent or avoid flight delays and cancellations. In this study, we forecast whether a given flight will be delayed or not using machine learning models such as Logistic Regression, Decision Tree Regression, Bayesian Ridge, Random Forest Regression, and Gradient Boosting Regression. The study also emphasizes the significance of error calculation in predicting flight delays. In order to evaluate the effectiveness of our model, we examined various error metrics, and we discovered that the mean absolute error offers the most accurate representation of the errors. The outcomes show that our machine learning approach is highly accurate in forecasting flights delays. Our model can accurately predict delays ranging from a few minutes to several hours.