The object of this thesis is the development of predictive algorithms for the support of investment decisions in Private Equity. In particular, the developed methodologies attempt to estimate the probability associated to a future state of a company, distinguishing among four possibilities: being acquired, go bankrupt, stay private, go public. Further, we model the evolution in time of the probability for a company to undertake an Initial Public Offering (IPO), which is one of the major events in the Private Equity market. The first objective has been realized by means of Machine Learning techniques, using Random Forest and Neural Networks models, in both the Multilayer Perceptron and Long Short-Term Memory configurations.