In the lending industry, investors provide loans to borrowers in exchange for the promise of repayment with interest. If the borrower repays the loan, then the lender would make profit from the interest. However, if the borrower fails to repay the loan, then the lender loses money. Therefore, lenders face the problem of predicting the risk of a borrower being unable to repay a loan. In this study, the data from Lending club is used to train several Machine Learning models to determine if the borrower has the ability to repay its loan. In addition, we would analyze the performance of the models (Random Forest, Logistic Regression, Support Vector Machine, and K Nearest Neighbors). As a result, logistic regression model is found as the optimal predictive model and it is expected that Fico Score and annual income significantly influence the forecast.