Loan Prediction Analysis Using Innumerable Machine Learning Algorithms

Internet loan fraud has emerged as a significant challenge in the financial industry, causing substantial economic losses and undermining trust in digital lending platforms. Applicants’ information includes income, level of education, employment status, loan amount, and credit histories, among other relevant variable information. Data preparation should start with the essential data pre-processing tasks: imputation of missing values for variables, encoding categorical values, and normalizing of numerical features. The dataset is divided between training and testing subsets when evaluating the model. The XG-Boost classifier is utilized to predict loan approvals, with its performance gauged by metrics like accuracy, precision, recall, and F1 score. Hyperparameter tuning is used to improve model performance. Results suggest that the XG-Boost classifier provides high accuracy for loan approval predictions. Also, the feature importance analysis suggests key factors that have a strong influence on the model's decisions, showing the potential of the algorithm in helping financial institutions make better decisions. This study shows the scalability and effectiveness of XGBoost for practical credit risk prediction tasks.