Loan Approval Prediction through Machine Learning and Visualization: Insights from Gradient Boosting and Power BI

This study looks at integration of machine learning (ML) models with business intelligence tools for improved loan approval predictions to enhance better decision-making processes. We use a dataset that contains borrower characteristics such as income, credit history, and loan status, which we implement and test algorithms such as Gradient Boosting in predictive modeling. This integrates Microsoft Power BI, wherein all stakeholders can visualize data relations and trends through interactive dashboards. This will show potential in ML systems to assist in streamlining advisory procedures within financial organizations. Hence, this research uses its results to provide deeper insights regarding factors that influence loan decisions for financial institutions. The proposed method can help in data-driven strategies along with the better understanding of the trends in loan approvals; therefore, it improves overall analytical capabilities of decision-making