Bank Customer Segmentation Model Using Machine Learning

Banks generally carry out marketing strategies by offering deposit products directly to customers. However, this method is less effective because it requires individualized communication without considering the customer's interest in the product offered. Therefore, this research aims to categor...

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Bibliographic Details
Published in:Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Vol. 13; no. 1
Main Authors: Bunga Tiara, Vira, Siregar, Amril Mutoi, Kusumaningrum, Dwi Sulistya Kusumaningrum, Rohana, Tatang
Format: Journal Article
Language:English
Published: Universitas Pendidikan Ganesha 31.03.2024
Subjects:
Classification
Deposit Bank
Machine Learning
PCA
Supervised Algorithm
ISSN:2089-8673, 2548-4265
Online Access:Get full text
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Summary:Banks generally carry out marketing strategies by offering deposit products directly to customers. However, this method is less effective because it requires individualized communication without considering the customer's interest in the product offered. Therefore, this research aims to categorize the classification of bank customers into Yes and No. This research uses a dataset of bank deposits taken from KTM. This research uses a bank deposit dataset taken from Kaggle, the data consists of 11162 rows with 17 attributes.  PCA technique was used for feature selection which was optimized by reducing the dimensionality of the dataset before modeling. It was found that the best model accuracy was SVM RBF kernel with C parameters achieving 80.51% accuracy and ANN 80.78%, but ANN showed a higher ROC graph than SVM because ANN performance results were faster than SVM. Thus, the overall performance measurement of ANN is much better.
ISSN:2089-8673
2548-4265
DOI:10.23887/janapati.v13i1.75233