Intellecutual data visualisation

Abstract

Globalization and technological advances has created an extremely competitive market. This also hasan impact on the banks. In recent years, banking and direct database marketing have become animportant strategy for understanding customer needs. The success rate of banking marketing dependson the achieved results and decisions. In order to make more accurate predictions, statistical tools andmethods are been used. This report examines how to use machine learning techniques to analyze and make predictions inbanking marketing using existing dataset. The purpose of building the models is to predict whether theclient will subscribe for a term deposit. This report presents the different stage of data analysis such asdata preparation and cleaning, building the models and model testing. Finally, the results of machinelearning techniques are evaluated and analysed. Although there is no significant difference in thedecision tree algorithm’s accuracy, C5.0 achieved a higher percentage.Linear regression modelpresents the relationship between quantitative features.