A Study on an Effective Model for Predicting Flight Delay
Rebecca Judaist1, Praveen R2, Rakshitha S3, Vinod Kumar S4, Meghana M5
1Meghana M, Assistant Professor, Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
2Rebecca Judaist, UG student, Department of Computer Science & Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
3Praveen R, UG student, Department of Computer Science & Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
4Rakshitha S, UG student, Department of Computer Science & Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
5Vinod Kumar S, UG student, Department of Computer Science & Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
Manuscript received on 19 March 2022 | Revised Manuscript received on 01 April 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 1-3 | Volume-2 Issue-2, July 2022 | Retrieval Number: 100.1/ijsepm.D9013071422 | DOI: 10.54105/ijsepm.C9013.071422
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: This Model’s Initial Stage Uses Binary Classification to Predict Flight Delays
Scope of the Article: Software Implementation and Maintenance