The numerous characteristics of customers are often kept in bank databases, which are utilized to understand who they are. But it has been found in recent years that utilizing different Data Mining and Feature Selection (PCA) methods, customer traits and other factors connected to bank services have a big influence on consumers' decisions. Business analytics is an approach to conducting business that uses transactional data from an organization to acquire knowledge of how business operations can be enhanced by employing data mining methods to determine existing patterns that a firm can incorporate to generate significant data-driven choices to choose significant variables. In this project, we apply data mining techniques for the prediction of long- term bank deposits employing a well-known bank data collection. From PCA it is seen that customers’ income level, pout come, p days, and previous (first PC) in general, may seem to have a higher impact on prospective clients, but this is indeed not the real. Also, the Banks’ prior campaign and the social elements (Age, Marital Status, Education, Campaign, Duration) of the clients are primarily essential compared to other variables. Again k-means clustering is employed with reduced data by PCA to determine groups of potential customers which gives 87.76% accuracy scores.
Professional Studies (MS)
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Graduate Programs & Research (Dubai)
Al Hammadi, Mohamed, "Identifying Prospective Clients for Long-Term Bank Deposit" (2022). Thesis. Rochester Institute of Technology. Accessed from