Flight Arrival Delay Prediction Using Deep Learning
Decision Tree; Deep learning; Dense Neural Network; Lasso; NAS; Random Forest; Regression; Ridge; RMSE (Root Mean Square Error)
This project is aimed to solve the problem of flight delay prediction. This problem does not only affect airlines but it can cause multiple problems in different sectors i.e., commercial (Cargo aviation), passenger aviation, etc. There are a number of reasons why flights can be delayed, with weather being the main one. Our goal in this study is to forecast flight delays resulting from a variety of reasons, such as inclement weather, delayed aircraft, and other issues. The dataset gives itemized data on flight appearances and postponements for U.S. air terminals, classified via transporters. The information incorporates metrics such as the number of arriving flights, delays over 15 minutes, cancellation and diversion counts, and the breakdown of delays attributed to carriers, weather, NAS (National Airspace System), security, and late aircraft arrivals. For the purpose of predicting flight delays, the outcomes of several machine learning algorithms are examined, including Ridge, Lasso, Random Forest, Decision Tree, and Linear regression. With the lowest RMSE score of 0.0024, the Random Forest regressor performed the best across all scenarios. A deep learning model using a dense neural network is built to check how accurate a deep learning model will be while predicting the delay and the result was an RMSE score of 0.1357. 2024 IEEE.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
Institute of Electrical and Electronics Engineers Inc.
Sharma N., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Pune, India; Vijayalakshmi S., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Pune, India