Predicting flight delays is crucial for improving airline efficiency and customer satisfaction. Flight delays, caused by factors like bad weather, mechanical issues, and late arrivals, can negatively impact customer experience. This project aims to develop a predictive model for on-time flight arrivals using flight and weather data. We employ machine learning algorithms such as Decision Tree Regression, Bayesian Ridge, Random Forest Regression, and Gradient Boosting Regression to predict flight delays.

Additionally, we incorporate Support Vector Machine (SVM) and enhance its accuracy using hyper tuning techniques. By optimizing these models, we aim to achieve higher accuracy in predicting flight delays. This model can assist airlines in making better decisions, ultimately leading to improved operational efficiency and customer satisfaction.

Keywords: Flight delays, machine learning, predictive model, Decision Tree, Random Forest, Support Vector Machine, hyperparameter tuning.