Published Paper Details:

This research paper aims to predict customer interest in purchasing vehicle insurance from a health insurance company using data science techniques. The study uses data collected from health insurance holders of the company and employs data processing, analysis, and visualization techniques to prepare the data for machine learning models. Three models, namely logistic regression, Naive Bayes, and random forests were trained on the data, and their performance matrices were compared to determine the best-performing model. The study concludes that the random forests model outperformed the other two models in predicting customer interest in purchasing vehicle insurance. The results of this study have practical implications for health insurance companies seeking to expand their service offerings to customers. The study contributes to the growing field of data science in the insurance industry and highlights the potential benefits of using machine learning models to predict customer interests. The results of this research can aid insurance companies in developing targeted marketing strategies and improving customer satisfaction.