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Road Accidents have a huge economic and societal impact costing hundred of billions of dollar every year. A study by the Department of Transportation's, National Highway Traffic Safety Administration (NHTSA), placed a price tag of over a quarter-trillion dollars in 2010. A large part of these losses is caused by a small number of serious accidents. Reducing accidents, especially these serious accidents, is an important challenge.
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Objective of our project is to identify the key factors affecting the accident severity. Also, train a model which predicts:
- Accident Severity - a number between 1 to 4, where 1 indicates the least impact on traffic and 4 indicates a significant impact on traffic
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We took the literature review and references from Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights and Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol
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In the initial stages of the project, we did the EDA of the above datasets and did feature engineering to come up with the most optimal dataset.
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Created baseline models:
- Decision Tree
- Logistic Regression
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Main Models:
- Decision Tree
- Random Forest
- SVM
- Logistic Regression
- ADA Boost
- XG Boost
- Neural Networks
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The files in the folder
Model Training Codeare used for creating the model dumps of the above 2+7 models and the weights are saved. -
Data and Trained models can be found here : https://drive.google.com/drive/folders/1D8hd2GPEJYF-BKjkfV1-7CILVjdi-zDt?usp=sharing