A Study on A Car Insurance Purchase Prediction Using Machine Learning with Full Stack Web Development

Abstract

Car insurance companies face significant challenges in identifying potential customers who are likely to purchase insurance policies. With the rapid growth of data generated from customer demographics, vehicle details, and behavioral patterns, machine learning techniques can be effectively used to predict insurance purchase decisions. This project focuses on developing a car insurance purchase prediction system using supervised machine learning algorithms integrated with a full stack web application. The system analyzes historical customer data to identify key factors influencing insurance buying behavior. A predictive model is trained to classify whether a customer will purchase insurance or not. The web application provides an interactive interface for insurers to input customer data and view predictions in real time. This approach improves decision-making accuracy and marketing efficiency.

Overall, the system enhances customer targeting and reduces operational costs.