Improved Supervised Machine Learning for Predicting Auto Insurance Purchase Patterns

This article presents a predictive model using supervised machine learning, highlighting the importance of advanced optimization algorithms. Our approach focuses on a nonsmooth loss function known for its effectiveness in supervised machine learning. To ensure desirable properties such as second derivatives and convexity, and to handle outliers, we use a smoothing function to approximate the loss function. This enables the development of robust and stable algorithms for accurate predictions. We introduce a new surrogate smoothing function that is twice differentiable and convex, enhancing the effectiveness of our methodology. Using optimization techniques, especially stochastic gradient descent with Nesterov momentum, we optimize the predictive model. We validate our algorithm through a comprehensive convergence analysis and extensive comparisons with two other prediction models. Our experiments on real datasets from insurance companies demonstrate the practical significance of our approach in predicting auto insurance customer interest.