Classification of Customer Decisions in Opening Deposits Using the C4.5 Algorithm Implemented in Python

Chaerul Hidayat; Fabian Eka Prasetyo Fabian; Elkin Rilvani Elkin

doi:10.59934/jaiea.v5i1.1384

Authors

Chaerul Hidayat Universitas Pelita Bangsa
Fabian Eka Prasetyo Universitas Pelita Bangsa
Elkin Rilvani Universitas Pelita Bangsa

DOI:

https://doi.org/10.59934/jaiea.v5i1.1384

Keywords:

Data Mining, C.45, Classification, Pyton, Deposits, Banking

Abstract

The banking industry needs to understand customer behavior to improve marketing strategies, particularly for deposit products. This study aims to develop a classification model to predict customer decisions in opening deposits using the C4.5 decision tree algorithm. The dataset consists of secondary banking data, including attributes such as age, occupation, marital status, education, account balance, and deposit decisions. The research adopts the Knowledge Discovery in Databases (KDD) process, from data cleaning to model implementation using Python and the chefboost library. Based on 11,162 customer records, the model achieved 56.69% accuracy, 55.63% precision, and 42.50% recall. Evaluation through a confusion matrix resulted in 2,248 True Positives (TP), 4,080 True Negatives (TN), 1,793 False Positives (FP), and 3,041 False Negatives (FN). The findings suggest that the C4.5 algorithm serves as a baseline approach for predicting customer behavior in deposit decisions. However, further improvements are needed to enhance its performance. This study contributes to the development of data-driven decision support systems in the banking sector.

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