Developing a Credit Approval Determination Model Using Principal Component Analysis with Machine Learning Techniques
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Abstract
This research presents a model and tests the performance of a credit approval determination model using principal component analysis with machine learning techniques. Four algorithms were tested: Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine. 441,335 examples of lending data. There are 4 model performance test results: Accuracy, Precision, Recall, and F-Measure.
The result of Accuracy from the experiment found that Random Forest model The best efficiency is 92.9%, followed by Support Vector Machine and Decision Tree was 87.0% and Naïve Bayes was 83.4%, respectively. It was found that reducing the data dimensions resulted in improved model performance by eliminating insignificant features and solves the problem of the traditional credit approval model that considers the old attribute variables. The model has improved performance by considering new attribute variables.
The results of the completeness Recall and the overall performance F-Measure from the experiment showed that Random Forest model provides the best performance as well as Accuracy was 99.64% and 99.35%, respectively, and the highest Precision was 99.07% for Random Forest Model and Decision Tree.
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