Enhancing Hyperparameters for Improved Flight Delay Prediction Using Machine Learning Algorithms

Flight delays present major problems for the aviation sector, affecting both operational efficiency and consumer happiness. Existing prediction systems have accuracy and flexibility limitations. The study provides an advanced machine learning-based method that makes use of a large dataset, combining historical flight and meteorological information. Random Forest, Gradient Boosting, and Support Vector Machines are used in the system, with hyperparameters optimized using Random search, Bayesian, and Grid search techniques. The results show that the proposed system outperforms the existing system in terms of prediction accuracy, obtaining 95% versus 90%. Precision, recall, and F1-score all exhibit gains, highlighting the suggested system's superiority. The paper methodically describes data collection, algorithm selection, hyperparameter optimization, and model evaluation, as well as an innovative approach to flight delay prediction. To summarize, the proposed system provides a big step forward, providing a more accurate and adaptive system for improving operational efficiency and customer satisfaction in the aviation sector.