Bank Marketing Data Mining using CRISP-DM Approach

Abstract

We live in a world where vast amounts of data are collected daily. At the bank, a large amount of data is recorded about their customers. This data is commonly used to create, maintain relationships and direct connections with customers to target them individually in the sale of certain products or banking offerings. Analyzing such data is an important need. This paper describes data mining approaches aim to build a predictive model that labels data into a predefined class. To define the processes and tasks that you must do to develop a successful Data Mining project using CRISP-DM Framework. This study will use Multilayer Perceptron and logistic regression as data mining technique. In this paper, the resulting model allows overfitting, so it can be avoided using cross validation. the model that provides the greatest average benefit is the Multilayer perceptron model with a 70% percentage split.