I2.02 - Block Time Prediction using Machine Learning Techniques

Sabre

Currently, the airline industry loses ~$22B annually due to many things, delay propagation being a large contributor. This project uses Machine Learning tools such as Scikit Learn in Python to predict the block time, total taxi time plus air time, between two airports in the US to lower the probability of delay and minimize losses within the industry. Four regression models were developed to analyze this problem, each was tested with our dataset and compared to select the best performing model. With the selected model, a Graphical User Interface was created to allow user interaction with the model and get real-time predictions for airports within the US.