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academic research - introductionThe Effect of machine learning algorithms Advancement Level over their performance when solving Business Analytics problems October 2019 Yossi HohashviliAcademic adviser:  Adir Even, PhD, Ben-Gurion university of the NegevAdditional adviser: Ephraim Goldin, CEO and founder, G-statAbstractOne of the most severe problems in Business Analytics (BA) is automation of decision-making processes. this automation is of great relevance do to the pros and cons that may accrue using a specific methodology. at a time where data, technology and analytics occupy a major place in the business sector, organization's ability to keep pace with the technological development of the big data world is critical. For this reason, the management team must identify an optimal alternative for solving a BA problem. In order that the director will make an optimal decision, he must identify the type of problem, tools and resources at his disposal to achieve the purpose of the business. The inability of an organization to make correct and reasonable strategic decisions that consider technological development may cause the organization to fail in its objectives.In recent years, advanced Machine Learning (ML) algorithms such as Deep Learning (DL) has been empirically proven themselves superior to traditional ML and classical statistics algorithms for solving unstructured data problems, such as image, text and sound processing. On the other hand, there are few studies that regards the superiority of DL over other algorithms mentioned above or advanced ones such as XGboost, when tackling problems with structured data, which is known to characterize business information to a large extent. The following research will examine the possibility of expanding the scope of learning algorithms used for BA problems with the use of advanced algorithms for solving complex classification and regression problems that characterize the BA world. we will achieve this, by conducting an ex