Predicting Creditworthiness of Smartphone Users in Indonesia during the COVID-19 pandemic using Machine Learning

In this research work, we attempted to predict the creditworthiness of smartphone users in Indonesia during the COVID-19 pandemic using machine learning. Principal Component Analysis (PCA) and Kmeans algorithms are used for the prediction of creditworthiness with the used a dataset of 1050 respondents consisting of twelve questions to smartphone users in Indonesia during the COVID-19 pandemic. The four different classification algorithms (Logistic Regression, Support Vector Machine, Decision Tree, and Naive Bayes) were tested to classify the creditworthiness of smartphone users in Indonesia. The tests carried out included testing for accuracy, precision, recall, F1-score, and Area Under Curve Receiver Operating Characteristics (AUCROC) assesment. Logistic Regression algorithm shows the perfect performances whereas Naïve Bayes (NB) shows the least. The results of this research also provide new knowledge about the influential and non-influential variables based on the twelve questions conducted to the respondents of smartphone users in Indonesia during the COVID-19 pandemic.