Open AccessA Study on an Effective Model for Predicting Flight Delay
Author(s) -
M Meghana,
Rebecca Judaist,
R Praveen,
S Rakshitha,
Vinod Kumar S
Publication year - 2022
Publication title -
indian journal of software engineering and project management (ijsepm)
Language(s) - English
Resource type - Journals
ISSN - 2582-8339
DOI - 10.54105/ijsepm.c9013.071422
Subject(s) - decision tree , computer science , logistic regression , revenue , scheduling (production processes) , operations research , binary classification , support vector machine , machine learning , operations management , engineering , business , finance
Amongst the most significant business concerns that airline companies face is the considerable expenses related to airlines being delays caused due to natural events and operations and maintenance flaws, which is an additional expense for the airlines, having caused scheduling and operations problems for end-users, likely to result in a negative revenue and customer displeasure. We used supervised machine learning approaches in this study to develop a two-stage prediction models for forecasting flight on-time performance. This model's initial stage uses binary classification to predict flight delays, while the second phase uses regression to estimate the delay's duration in minutes. The proposed research compares the effectiveness of decision tree classifier to logistic regression. Based on the created model, the outcomes of this simulation disclose projected congestion in airports, considering hour, day, climate, and so on. As a result, there will be less time spent waiting.