Climate change is one of the biggest and most worrying problems in the current world. Renewable energy sources are one of the main tools that will allow the humanity to fight against it. More precisely, floating wind turbines offer unprecedented amounts of generated power compared to their onshore or offshore (bottom-fixed) counterparts. The technology is however, at an early stage of development, with a lot of improvements to be made and countless fields of study. This project aims to study the behavior of the scale model of a floating wind turbine by elaborating several statistical models that can predict some of its most important statistical metrics. These statistical models are dependent on the wind speed and the blade pitch angle of the wind turbine. Additionally, a periodicity analysis of the wind turbine is also made in order to determine if there are frequencies associated with it at different wind speeds and pitch angles. In this work, a data preprocessing phase is carried out with the aid of statistics and graphical representations. Then, two studies are made: a periodicity analysis by several Fourier Transforms, and multiple regression supervised models. The supervised models used were: Linear Regression, Polynomial Regression, Ridge Regressor, Huber Regressor, Gaussian Regressor and a Neural Network (MLP Regressor). Most of the supervised models were very successful and could be used to create a virtual model of a wind turbine. The periodicity analysis was also successful and was consistent with the physical analysis of the wind turbine.