Data mining techniques are used more and more in the economic field. Such as the prediction of certain economic indicators, the discovery of hidden information, problems or finding problems in the industrial sector, as well as in relations with customers through the study of their data and behaviors to improve the cost-effectiveness of customer relationships or attract new customers. Data Mining techniques are classified into two categories: supervised and unsupervised. This paper focus only on the first techniques for solving Data Mining tasks such as: Decision Trees, Regression, Neural Networks and Support vector machines (SVM). The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.

Data Mining Regression Decision Trees Neural Networks SVM Supervised learning techniques