• Padamati Hari Krishna Reddy
• Nallamada Rishika
• Mora Sai Srujan Reddy
• T Panduranga

The civil aviation industry's rapid growth has highlighted the importance of addressing flight delays, which have serious economic consequences for airlines and related businesses. Predicting delays for specific flights is critical for airline strategic planning, airport resource allocation, insurance company policies, and itinerary planning. However, the multidimensional character and non-linear correlations of the factors causing flight delays provide substantial hurdles to effective prediction. Variations among geographies, airports, and even differences in airport or airline protocols increase the complexity of prediction jobs. To address the limitations of existing prediction models, this research provides a new flight delay prediction framework with improved generalization capabilities and accompanying machine learning classification techniques. This model uses temporal and spatial variables across several dimensions, including elements such as previous flight patterns, departure and arrival airport circumstances, and general flight dynamics along specific routes. The model is trained using historical data and tested against the most recent real data, providing a promising method to resolving the complexities of flight delay prediction in civil aviation

Flight Delay Prediction, Machine Learning Models, Arrival Time Accuracy, Weather Data Correlation, On-time Performance, Airline Operations, Data Science Approach, Feature Engineering, Predictive Modelling, Performance Evaluation.

"An Overview On Prediction Of Flight Delays Using Machine Learning Classifiers With Error Calculation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.c875-c880, April-2024, Available :http://www.jetir.org/papers/JETIR2404293.pdf

"An Overview On Prediction Of Flight Delays Using Machine Learning Classifiers With Error Calculation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppc875-c880, April-2024, Available at : http://www.jetir.org/papers/JETIR2404293.pdf