Aviation Data Analysis and Fatal Accident Prediction Using Machine Learning

Introduction

Airplanes are one of the safest and most popular modes of transportation globally. However, even a single airplane accident can result in significant loss of life. Despite advancements in aviation safety, accidents still occur, making it essential to analyze past data and develop tools to predict and prevent future accidents.

This project focuses on analyzing aviation accident data to identify patterns and predict the likelihood of fatal accidents using machine learning techniques. By understanding the key factors contributing to these accidents, we aim to create a predictive model that can assist in improving aviation safety and preventing mishaps.

Project Approach

1. Data Collection and Preparation

   • We used a publicly available aviation accident dataset from Kaggle. The dataset includes information like accident dates, locations, aircraft types, operators, causes, and fatalities.
   • The data was cleaned, preprocessed, and prepared for analysis, including exploratory data analysis and feature selection.

2. Machine Learning Models

   • Three machine learning regression models were tested to predict accident outcomes:
     • Linear Regression
     • Random Forest Regressor
     • XGBoost Regressor
   • These models were trained and evaluated using the Mean Absolute Error (MAE) metric to determine their accuracy in predicting accidents.

3. Results and Insights

   • The XGBoost Regressor outperformed the other models, achieving the lowest MAE of 252.1839. This indicates it is the most accurate model for predicting fatal accidents in civil aviation.
   • Insights from this analysis can help identify high-risk scenarios and guide stakeholders in implementing safety measures.

Conclusion

By leveraging machine learning, this project demonstrates how data analysis can be used to predict and reduce aviation accidents. The XGBoost Regressor proved to be the most effective model, providing reliable predictions that can aid airlines, regulators, and manufacturers in improving aviation safety. With further refinement and integration into real-world systems, this approach could play a vital role in making air travel even safer.