Predicting Fatalities in Air Accidents using CHAID XG Boost Generalized Linear Model Neural Network and Ensemble Models of Machine Learning
Nikita Pande1, Devyani Gupta2, Jitendra Shreemali3, Prasun Chakrabarti4
1Nikita Pande, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
2Devyani Gupta, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
3Jitendra Shreemali, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
4Prasun Chakrabarti, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
Manuscript received on 15 March 2020 | Revised Manuscript received on 22 March 2020 | Manuscript Published on 30 March 2020 | PP: 35-39 | Volume-9 Issue-3S March 2020 | Retrieval Number: C10090393S20/20©BEIESP | DOI: 10.35940/ijeat.C1009.0393S20
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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: The study examines the historical data of about 4700 air crashes all over the world since the first recorded air crash of 1908. Given the immense impact on human beings as well as companies, the study aimed at utilizing Machine Learning principles for predicting fatalities. The train-test partition used was 75-25. Employing the IBM SPSS Modeler, the machine learning models used included CHAID model, Neural Network, Generalized Linear Model, XGBoost, Random Trees and the Ensemble model to predict fatalities in air crashes. The best results (90.6% accuracy) were achieved through Neural Network with one hidden layer. The results presented also include comparison of the predicted versus observed results for the test data.
Keywords: Neural Network, CHAID, Ensemble Model, XG Boost Model, Air accidents, Fatalities.
Scope of the Article: Machine Learning