This research uses data from Kaggle, which consists of 32,581 rows and 12 columns, to develop a credit worthiness prediction model. The aim of the research is to identify factors that influence creditworthiness and develop a model that accurately predicts whether a borrower is creditworthy or not. The research uses the Random Forest method and involves data pre-processing steps, including imputation of missing values and handling of outliers, as well as dividing the dataset into training data and test data. The results show that the model achieves an accuracy of 93.28%, with the best parameters 'max_depth': 30, 'min_samples_leaf': 1, 'min_samples_split': 2, and 'n_estimators': 100. This research contributes to the understanding of creditworthiness and development Prediction models that can be used by financial institutions to make more precise credit decisions.