Flight Delay Prediction System

The project involved preprocessing and cleaning historical flight data, including handling missing values and transforming time-based features into usable formats. Feature engineering techniques such as encoding categorical variables and scaling numerical data were applied to improve model performance. Multiple machine learning models were trained and evaluated using metrics like accuracy and confusion matrix, with the best model selected for deployment. The final system was integrated into a simple web interface that allows users to input flight details and receive real-time delay predictions along with probability insights.