Flight Delay Prediction for Indian Air Carriers with Explainable Artificial Intelligence

The aviation industry plays a crucial role in the world's transportation sector, and a lot of businesses rely on various airlines to connect them with other parts of the world. Flight delays are gradually increasing and bring more financial difficulties and customer dissatisfaction to airline companies. The present research takes into consideration the flight data for various Indian carriers, Airport scores, Airlines Ratings, and weather information to build a robust prediction system. This research is done in three phases. In the first phase, Classification models like Logistic Regression, Decision Tree, Decision Jungle, and Neural Network Classifier are used to predict if the flight is delayed more than 15 minutes. The most effective model is chosen, and the primary causes of the flight delay are identified using the model. These factors include both controllable and uncontrollable factors resulting in the flight delay. In the second phase, Regression models like Linear Regression, Linear Regression with Principal Component Analysis (PCA) technique, and Neural Network Regressor model are used to predict the time by which the flight is getting delayed. The third phase of the research focuses on the trips with significant delays in the chosen dataset, and actionable insights are identified and explained for each trip using the Explainable Artificial Intelligence (XAI) tool with the Local Interpretable Model-agnostic Explanations (LIME) package. The Neural Network Classifier is identified as the best non-linear model for the dataset under consideration, with an accuracy of 92.5% and a precision of 73.6% for predicting delayed flights. With the lowest RMSE of 3.52, MAE of 2.20, and maximum coefficient of determination(R square) of 93.44, the Linear Regression model with Principal Components stands out to be the best model for predicting delay in flight arrival time.