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Accident Severity Prediction - Machine Learning Project

  • 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.

  • 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
  • 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

  • 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.

  • Created baseline models:

    • Decision Tree
    • Logistic Regression
  • Main Models:

    • Decision Tree
    • Random Forest
    • SVM
    • Logistic Regression
    • ADA Boost
    • XG Boost
    • Neural Networks
  • The files in the folder Model Training Code are 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

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