FLIGHT ACCIDENT SEVERITY PREDICTION

FLIGHT ACCIDENT SEVERITY PREDICTION

Dhanashree Wagh, Dhanashree Patil, Rishi Rathod,

Saurabh Vadnere, Prof. Sangeeta Alagi

University of Pune, Computer Department, Genba Sopanrao Moze College of Engineering, Pune, Maharashtra, India

Under the Guidance of Prof. Sangeeta Alagi, Genba Sopanrao Moze College of Engineering, Pune, Maharashtra, India

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ABSTRACT - Nowadays transport of airways has been playing a key role in every industry airline and passengers safety is the first concern we should take care of, Various Safety checks are done continuously 24/7 manually, and every safety measure and precaution are been taken care of by the airline team but still some case accidents due to various reasons like pilot error, air traffic controller error, design and manufacturer defects, maintenance failures, sabotage, or inclement weather etc. The prediction of flight accident severity is crucial for enhancing aviation safety and preventing future accidents. In this research paper, we propose a machine learning-based approach for predicting the severity of flight accidents. Our proposed approach involves the use of feature engineering techniques to extract useful information from the accident dataset, followed by the application of various machine learning algorithms to predict the accident severity. We use a publicly available dataset of flight accidents to evaluate the performance of our proposed approach

Key Words:  Error Correction, Safety Management, Flight Training Data, Random Forest, Airplane Crash, SVM, python programming