Modelo de aprendizaje automatizado para el cálculo de la prima vehicular y segmentación del consumidor.

Abstract

This research uses the concepts and algorithms of Machine Learning, Multiple Linear Regression and K-means, to improve the accuracy in the calculation of vehicle premiums and the segmentation of their respective clients. Based on this, an Automated Learning model was developed, which predicts through variables such as sex, vehicle value, current premium value, among others, how much the premium would be to be paid by each client at the time of insuring the vehicle. In addition, with these same variables the model also segments clients depending on their characteristics, thus giving greater personalization of vehicle premiums, and determining the categories of possibility to pay in clients, thus allowing future clients to have similar characteristics. one can have a clear idea of how much they should pay for the value of the vehicle premium.