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An Approach of Supervised and Unsupervised Machine Learning Model for E-CRM Bank’s Marketing


Abubakr Mustafa Eltahir, Professor. Tarig Mohamed Ahmed, Dr. Amgad Atta Abdelmageed Mohammed, Dr. Anwer Mustafa Mohamedsalih Hilal, Dr. Tarig Abdalkarim Abdalfadil


Vol. 22  No. 4  pp. 625-636


This paper displays a combination data mining algorithm within a Bank's database system, which is started by providing a data mining application that characterizes a solution for one of the problems that exist in the bank's database systems, which is creating a new electronic means to increase the effectiveness of the mechanism to attract the community of depositors and their resources to banks. After that, it fits into the bank system to perform the solution inside the bank's database system. This method is then repeated every day at a specific time to improve and enhance the bank system by providing data mining methods that support different departments in the bank, particularly in the Marketing department and Decision-Makers employees, to execute a specific marketing decision. A hybrid model, of unsupervised machine learning, was designed, represented by the (K. means) algorithm and its data outputs were used in the supervising machine learning represented by the (multi-class decision jungle) algorithm, to determine which, cluster the customer belongs to and whether he will subscribe in a term deposit or not, and how much he will participate in deposit or not participate in a term deposit. which provides these applications with a conceptual and scientific approach to link the resulting application in the bank's database system, it is worth noting that this experience can be used in bank databases with data mining technologies. The researcher found that; data of data algorithms can be applied to the bank system as a smart banking system and to adapt data mining algorithms to any database system, regardless of the system's modern or old, in addition to that data mining algorithms contribute to producing important results that contribute effectively to make effective strategic decisions. In the conduct of the banking process.


Data Mining; Bank system; Database; combination; two-class boosted decision tree; Term deposit.