Predicting Causes of Airplane Crashes using Machine Learning Algorithms
Ved Prakash Gupta1, M Sajid Mansoori2, Jitendra Shreemali3, Payal Paliwal4
1Ved Prakash Gupta, B.Tech, Department of CSE, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
2M Sajid Mansoori, B.Tech, Department of CSE, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
3Jitendra Shreemali, Professor, Department of Computer Science and Engineering, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
4Payal Paliwal, B.Tech Degree, Department of Electronics and Communication Engineering, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
Manuscript received on 24 February 2020 | Revised Manuscript received on 10 March 2020 | Manuscript Published on 18 March 2020 | PP: 144-147 | Volume-8 Issue-6S March 2020 | Retrieval Number: F10270386S20/2020©BEIESP | DOI: 10.35940/ijrte.F1027.0386S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Considering the immense cost of air crashes, the study examines the causes of crashes of aircrafts based on reported findings for the crash. The dataset used for this study included data for all reported air crashes across the globe for the period from 1981 to 2019. The causes were classified into seven categories. Multiple machine learning algorithms were used to identify the best for predicting the likely cause of accident based on features available. The Machine Learning Models used are Auto Classifier, Tree-AS and XG Boost. Also the key predictors are identified for use by planners.
Keywords: Machine Learning, XG Boost, Neural Network, Deep Learning, Tree-AS.
Scope of the Article: Machine Learning