Air transportation occurrence data

The aviation system is safety-critical by nature, and any occurrence of an incident or accident can lead to the loss of human life and monitoring. The International Civil Aviation Organization (ICAO) emphasizes that every flight must take off and land safely, a goal that attains more than 126,000 times per day. Aviation safety issues and identify risks within the Air transportation system mishaps and accidents continue to occur despite major advancements, underscoring the necessity of strong safety management systems. To address this, machine learning methods are used to predict potential safety concerns based on historical data. This study utilizes an 80-year dataset of occurrence reports from TSB to fill the research gap in aviation incident and accident prediction. This study determines a significant research gap in the prediction of aviation safety occurrences by analyzing the historical data. The main objective is to develop a Predictive model that will accurately classify incidents and accidents, thus reducing risks, improving safety measures and enhancing response strategies. Utilizing the machine learning techniques, textual data was processed utilizing Natural Language Processing (NLP) to analyze dataset. Three classifiers—Multinomial Naive Bayes, Random Forest, and Support Vector Machine (SVM)—were used with TF-IDF vectorization, and a 5-fold cross-validation was conducted to optimize the model performance and their accuracy. The developed models achieved the 90% above accuracies in predicting incidents and accidents, the 5-fold cross-validation with SVM classifier performing best achieved 98% accuracy in managing the dataset’s complexity. This predictive capacity is an important instrument for aviation safety management that is proactive. This research contributes to the field of aviation safety by providing a reliable, dependable prediction system for incidents and accidents, that makes use of sophisticated machine learning techniques and historical data. These findings provide the valuable insights for regulatory bodies and airlines, contributing to the enhancement of safety protocols, ensuring safer air travel and which will ultimately lead to safer skies.