Optimizing Customer Response Prediction in Auto Insurance: A Comparative Study of Machine Learning Models

This study examines various machine learning models to predict customer responses in the auto insurance industry. We focus on metrics like accuracy, precision, recall, and F1-score, carefully selecting threshold values to balance model performance with practical business applications. Our analysis reveals the XGB Classifier's superiority, achieving 99% accuracy and a 98% F1-score. We provide a comparative analysis of models, highlighting the XGB Classifier's strengths in handling complex data and its efficiency compared to other tested models, like Gaussian NB and Logistic Regression, which showed similar accuracies but varied in precision and recall. This study underscores the importance of choosing the right model and fine-tuning it for specific industry needs.