Modeling Vehicle Insurance Adoption by Automobile Owners: A Hybrid Random Forest Classifier Approach

This study presents a novel hybrid framework combining feature selection, oversampling, and machine learning (ML) to improve the prediction performance of vehicle insurance. The addresses class imbalance problem in binary classification tasks by employing principal component analysis for synthetic minority oversampling technique random forest ML classifier prediction. results demonstrate that proposed outperforms conventional approach achieves better accuracy. purpose this is provide insurance managers practitioners with insights into how accuracy decrease financial risks industry.