In the era of Big Data many companies are relying on data-driven decisions to contribute to the achievement of business targets. One of the most challenging yet interesting challenges a company faces is the ability to adopt and develop increasingly competitive and innovative methodologies. The role of new technologies is to extract valuable knowledge and derive data-driven insights to enhance business performance. This thesis project deals with a topic of relevance to the automotive industry: Predictive Analytics, which has become a cornerstone of modern business strategy, enabling companies to anticipate future trends, optimize operations and make data-driven decisions. Specifically, the aim of the project is the development and implementation of predictive models in order to accurately forecast the end-of-life costs of Extended Warranty contracts, for vehicles used in agricultural and construction sectors.