Severitys prediction of car accidents in PA and model comparison

Abstract

This study aims to provide an analytical and predictive framework for understanding and forecasting the probabilities of varying severities of traffic accidents in Pennsylvania, based on the US Accident dataset dated up to March 23. Initially, the dataset and variable descriptions are provided for comprehensive understanding. During the data preprocessing phase, variables undergo a thorough examination for missing values, and suitable imputation methods are selected for data completeness. Further, feature selection and data cleansing are executed to prepare the dataset for model training. The study predominantly utilizes three machine learning algorithms, Logistic Regression, Random Forest, and Support Vector Machine, to construct predictive models. The performance of these models is meticulously evaluated for accuracy and compared through specific data sampling techniques. Overfitting checks, feature importance elucidation, and in-depth discussions on model performance variations are also included. By navigating through this multifaceted analysis, the study aims to shed light on the strengths and weaknesses of different modeling approaches for this particular problem context, thereby providing valuable insights for future research endeavors.