Method with machine learning to carry out feasibility in data mining projects

Abstract

Currently, there are Data Mining techniques aimed at increasing the accuracy of the information and the agility in the analysis.  These are applied in the productive sector to characterize behaviors, based on the discovery of knowledge and, in this way, base decision-making in real and dynamic situations. Artificial intelligence (AI) drives research methods and data mining techniques for knowledge acquisition. For its use, the life cycle of data mining projects is followed, which involves stages of extraction, cleaning, preparation and transformation, modeling, and data evaluation. However, it is important to consider a feasibility study for data mining projects, with the objective of positively impacting organizations, by minimizing costly errors and guaranteeing an efficient distribution of resources, as well as the decision on the continuity of a project. This article presents a Machine Learning method to carry out feasibility in data mining projects, seeking to impact organizations by minimizing costly errors and guaranteeing an efficient distribution of resources.