<![CDATA[Loans or credit are one of the key factors in advancing the economy. One of them is encouraging business expansion which will have a direct impact on a country's economic growth. Banks and other financing institutions must be able to evaluate the borrower's ability to pay their debts based on the inherent risks to reduce the possibility of default. To this end, machine learning (ML) has emerged as a revolutionary tool in using advanced prediction methods to examine historical data based on customer behavior. This research investigates the application of ML in predicting loan outcomes by optimizing parameters in the Machine Learning algorithm. The ML algorithms examined in this research are Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), and XGBoost (XGB). Meanwhile, the technique used in hyperparameter tuning is Grid Search Cross Validation (CV). The results show that the algorithm's performance is more optimal than before, it can be seen that the LR algorithm experienced an increase in accuracy of 5%, KNN by 4%, RF by 3%, DT by 3%, and XGB by 2%. By including a default dataset based on customer behavior and optimized algorithm parameters, apart from being able to answer the alignment in previous literature in providing a deeper understanding of loan estimation, this research can also provide an understanding that hyperparameter techniques are worth trying to improve the performance of ML algorithms. So, it will be easier for financing institutions to determine the right loan scenario.]]>
Copyrights © 2024
