Flight data analysis and delay prediction system

Abstract

The airline industry faces significant challenges in managing flight delays, which inconvenience passengers and disrupt airlines. To address this problem, this study aims to develop an advanced flight analysis and prediction system that leverages machine learning algorithms and data visualization techniques. The project begins by collecting and preprocessing historical flight delay data, including information on departure time, origin, destination, and elapsed time. The data is then analyzed to identify patterns, trends, and relationships that contribute to flight delays. Various machine learning algorithms, such as decision trees, random forests, and support vector machines, are applied to benchmark their performance in predicting flight delays. To enhance the understanding and interpretation of the data, powerful data visualization techniques are employed. Graphs and charts are generated to visualize the relationship between different factors, such as departure time, origin, and destination. They also visualize their impact on flight delays. These visualizations provide stakeholders, including airlines, airport authorities, and passengers, with valuable insights for decision-making and proactive planning. Through the benchmarking process, the most accurate and reliable machine learning algorithm is identified for developing the flight delay prediction system. The chosen algorithm is then integrated into the system, allowing users to input relevant flight parameters and obtain real-time flight delay predictions. The system provides users with timely information to make informed decisions, such as adjusting travel plans, considering alternative routes, or preparing for potential delays. The developed flight analysis and prediction system has the potential to significantly improve operational efficiency for airlines. It can also optimize resource allocation for airport authorities, and enhance passenger experience. By accurately forecasting flight delays, stakeholders can minimize disruptions, improve customer satisfaction, and reduce financial implications. The system also contributes to the overall performance of the aviation industry by optimizing flight schedules, minimizing delays, and enhancing resource utilization.