Predicting Loan Repayment: Using Machine Learning

This study employs machine learning methods to show loan repayment situation. By means of borrower data analysis, we develop models with exact loan repayment prediction capacity.

Methodology:

1. Complete set of loan applications, incorporating to the point elements including credit score, earnings, loan amount, and loan status.
2. Many stages of data preparation help to prepare the dataset for These procedures comprise dataset cleansing, missing value handling, and categorical variable encoding.
3. Finding most to the point features for loan repayment forecasting is the main objective of feature selection.
4. Applying logistic regression, decision tree, random forest, and gradient boosting among other classification techniques results in a model.
5. Measurements of precision, recall, precision, accuracy, F1-score, and ROC-AUC helps one to evaluate the performance of a model.

According to the search, the Gradient Boosting model produces most exact loan repayment predictions. This study shows that machine learning in the field of financial services provides lenders with significant data to improve their capacity to evaluate loan risk.