Open AccessComparison of Random Forest, Logistic Regression, and MultilayerPerceptron Methods on Classification of Bank Customer Account Closure
Author(s) -
Husna Afanyn Khoirunissa,
Amanda Rizky Widyaningrum,
Annisa Priliya Ayu Maharani
Publication year - 2021
Publication title -
indonesian journal of applied statistics
Language(s) - English
Resource type - Journals
ISSN - 2621-086X
DOI - 10.13057/ijas.v4i1.41461
Subject(s) - logistic regression , random forest , computer science , feature selection , econometrics , multilayer perceptron , business , statistics , artificial intelligence , mathematics , machine learning , artificial neural network
The Bank is a business entity that is dealing with money, accepting deposits from customers, providing funds for each withdrawal, billing checks on the customer's orders, giving credit and or embedding the excess deposits until required for repayment. The purpose of this research is to determine the influence of age, gender, country, customer credit score, number of bank products used by the customer, and the activation of the bank members in the decision to choose to continue using the bank account that he has retained or closed the bank account. The data in this research used 10,000 respondents originating from France, Spain, and Germany. The method used is data mining with early stage preprocessing to clean data from outlier and missing value and feature selection to select important attributes. Then perform the classification using three methods, which are Random Forest, Logistic Regression, and Multilayer Perceptron. The results of this research showed that the model with Multilayer Perceptron method with 10 folds Cross Validation is the best model with 85.5373% accuracy. Keywords: bank customer, random forest, logistic regression, multilayer perceptron