Deposit subscribe Prediction using Data Mining Techniques based Real Marketing Dataset

TL;DR

This paper applies data mining techniques, specifically rough set theory and decision trees, to a real Portuguese banking dataset to improve marketing campaign efficiency and predict deposit subscription retention.

Contribution

It introduces a novel application of rough set and decision tree methods to enhance marketing strategies and feature selection in banking deposit prediction.

Findings

Reduced the number of features needed for prediction

Identified significant features influencing deposit subscription

Achieved improved prediction accuracy

Abstract

Recently, economic depression, which scoured all over the world, affects business organizations and banking sectors. Such economic pose causes a severe attrition for banks and customer retention becomes impossible. Accordingly, marketing managers are in need to increase marketing campaigns, whereas organizations evade both expenses and business expansion. In order to solve such riddle, data mining techniques is used as an uttermost factor in data analysis, data summarizations, hidden pattern discovery, and data interpretation. In this paper, rough set theory and decision tree mining techniques have been implemented, using a real marketing data obtained from Portuguese marketing campaign related to bank deposit subscription [Moro et al., 2011]. The paper aims to improve the efficiency of the marketing campaigns and helping the decision makers by reducing the number of features, that describes the dataset and spotting on the most significant ones, and predict the deposit customer retention criteria based on potential predictive rules.