Machine Learning for Project Management

Given the great propensity of companies to work on projects, today project management has become a real vital pillar for all organizations. The two fundamental activities that allow us to understand and analyse the evolution of a project are Project Monitoring and Project Control. Both are essential to analyse the project in their key features, so, in terms of costs, time and quality. A fundamental aspect to consider within a project is Risk Management, which often has a considerable impact on the estimates of a project both in terms of costs, and time, which are therefore updated several times before showing up with the desired result the final one. The two typical project management disciplines mentioned so far must act in parallel in project management, so that everything remains under control. From the analysis carried out in the literature, it can be seen that there are several methods for estimating budget and time in advance, but there is no one that established a method doing it with a high degree of accuracy because the risks are never taken into account. It was therefore this theoretical problem, stimulated by the curiosity to evaluate and optimize the estimation models integrated with the risk analysis to give way to the drafting of this dissertation which aims to take into account the risks within a project and thus give a more accurate beforehand evaluation for the Estimate at Completion (EaC), taking into account all the surrounding factors. Subsequently, it will also be possible to reschedule the timelines to have a more approximate estimated time during the initial phase.