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.
