Open AccessBank predictions for prospective long-term deposit investors using machine learning LightGBM and SMOTE
Author(s) -
Much Aziz Muslim,
Yosza Dasril,
A Alamsyah,
Tanzilal Mustaqim
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1918/4/042143
Subject(s) - computer science , profit (economics) , term (time) , process (computing) , investment (military) , data mining , machine learning , artificial intelligence , economics , physics , quantum mechanics , politics , political science , law , microeconomics , operating system
Banks try to get profit from society in various ways. One way is to use long-term deposit investment offers. If the product offering process for potential investors is not carefully considered, it will waste resources. Therefore, this study analyzes the accuracy of the predictions of consumers who have a high chance of participating in this program. The dataset used is historical bank data provided by Kaggle. In previous research, accuracy prediction has been carried out, but the accuracy is still low because it does not use a method to balance the class. Better accuracy can be improved using LightGBM and SMOTE methods. The test results with the number of testing data as much as 6590 and training data as many as 32950 show the highest accuracy of 90.63%.