Abstract: Flight delays are a significant concern for both passengers and airlines, leading to inconvenience, financial losses, and operational disruptions. This abstract presents a comprehensive approach to mitigating flight delays through the development of a full-stack web application leveraging big data and machine learning techniques.
The proposed system utilizes a vast array of data sources, including historical flight data, weather conditions, air traffic, airport congestion, and aircraft maintenance records. By integrating and analysing these diverse datasets, the application aims to identify patterns and correlations that contribute to flight delays. Machine learning algorithms play a pivotal role in predicting flight delays accurately. Through the application of supervised learning techniques such as regression, classification, and ensemble methods, the system learns from historical data to forecast the likelihood of delays for future flights. Additionally, advanced models capable of handling complex relationships and nonlinearities are employed to enhance prediction accuracy. The full-stack architecture of the web application encompasses both front-end and back-end components, ensuring a seamless user experience. The front-end interface provides users with intuitive features for inputting flight details, accessing delay predictions, and receiving real-time updates. Meanwhile, the back-end infrastructure manages data processing, model training, and prediction generation in a scalable and efficient manner.
The Flight Delay Prediction Full Stack Web Application represents a comprehensive solution for addressing flight delays through the synergistic integration of big data and machine learning technologies. By empowering stakeholders with timely and accurate predictions, the application has the potential to significantly mitigate the impact of flight delays on both passengers and airlines alike.
Keywords: Big data, Machine learning, Regression, Full stack architecture.
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DOI:
10.17148/IJARCCE.2024.13485
