Evaluations of Baseline Machine Learning Algorithms in Loan Application Tasks

The financial market is always changing rapidly, and it is crucial for people to monitor certain activities to ensure the overall safety of transactions or exchanges. The applications of machine learning have delved into many areas include but not limited to risk management, natural language processing, computer vision etc. This paper explores the application of five classic Machine Learning (ML) algorithms, including Decision Tree, Random Forest, Logistic Regression, XGBoost and Support Vector Machine (SVM) on loan application to test their raw performances on an imbalanced dataset, and comparing their results to determine which models behaves most ideally under very little optimization made. The project reveals common weaknesses for machine learning models when applying on some imbalanced dataset, where the recall and f1-score are not as ideal as accuracy and precision as they are biased toward the majority samples. However, models such as Random Forest does produce a desirable result, thus offering valuable reference under the context of determining the eligibility of loan applicants.