Resistance Spot Welding (RSW) process is a technique used to join overlapping and thin metal sheets, and it is widely employed in the automotive and railway sectors. In this process, a current flow passes through a pair of electrodes pressed against each other, utilizing the heat generated by Joule effect to perform welding. In such a process, maintenance costs significantly impact the overall expenses. Therefore, thanks to the dissemination of Industry 4.0 paradigms and the use of sensors collecting data from process signals, it has become possible to develop data-driven models for implementing predictive maintenance. In this thesis work, based on real data collected in an experimental campaign at the J-Tech laboratory of the Polytechnic University of Turin, regression models were developed to predict welding outcomes related to two target variables, namely the welding diameter and the tensile-shear load.