Severity Level Prediction and Finding Cause in Flight Accidents Using Machine Learning

The safety of the airlines and their passengers should be our top priority. Various safety checks are performed continuously and manually round-the-clock, and the airline team takes care of all safety precautions and measures, but there are still some cases of accidents due to a variety of factors. To improve aviation safety and stop future accidents, it is essential to estimate how severe a flying mishap would be. In this study, we provide a method that estimating the seriousness of flying incidents. Our findings show that the suggested method beats conventional machine learning methods, predicting the severity of aviation accidents with an accuracy of up to 85%. Our work stresses the value of enhancing the effectiveness of models for predicting the seriousness of aircraft accidents. The suggested method may be applied by regulators and specialists in aviation safety to improve aircraft safety by creating more potent accident prevention measures.