Road Accident Severity Predictor

This project applies machine learning to predict the severity of road traffic accidents (Slight, Serious, Fatal) using a real-world dataset from the UK.
By analyzing environmental, temporal, and vehicular features, the model helps identify factors contributing to accident severity and provides insights for improving road safety.

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Project Objective

To build a predictive model that classifies the severity of road accidents based on historical accident data and contributing factors such as speed limit, weather, lighting, and road surface conditions.

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Technologies & Tools

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Model Used: Random Forest Classifier

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Results

• Achieved ~85–89% accuracy on test data.
• Identified lighting conditions and weather as critical predictors of accident severity.
• Visualizations: Confusion Matrix, Severity Distribution, Feature Importance, and Correlation Heatmaps.

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Repository Structure

📂 Road-Accident-Severity-Predictor

┣ 📜 README.md ← clean project overview

┣ 📜 Road_Accident_Severity_Predictor.ipynb ← main code

┣ 📜 Accident.csv ← dataset

┣ 📜 Report.pdf ← final project report

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Future Work

• Handle class imbalance with SMOTE / class weights.
• Try Gradient Boosting and Deep Learning models.
• Deploy as a web dashboard for real-time predictions.

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References

• K. Kumar and A. Toshniwal, "A data mining framework to analyze road accident data," Journal of Big Data, vol. 2, no. 1, pp. 1-15, 2015.
• P. Chien, J. Hsu and C. H. Huang, "A road traffic accident severity prediction model using machine learning," IEEE Access, vol. 7, pp. 64410-64418, 2019.
• S. Sharma, S. K. Goyal, and A. K. Pandey, "Accident severity prediction using ensemble techniques," Procedia Computer Science, vol. 132, pp. 895–902, 2018.
• A. Baharudin and M. Al Mamun, "Analyzing accident data using machine learning approaches," Transportation Research Procedia, vol. 45, pp. 74-81, 2020.
• T. Yoon, K. Cho and J. Park, "Machine learning-based traffic accident severity analysis using high-dimensional data," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4085-4095, 2021.