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loan-prediction

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This project automates bank credit risk assessment using AI and machine learning models to predict loan defaults. It streamlines the credit process with predictive analytics, model evaluation, explainability (SHAP), and deployment readiness.

  • Updated May 29, 2025
  • JavaScript

This repository contains a machine learning-based predictive model for automating loan eligibility assessments. Using features such as demographic details, loan information, and credit history, the model predicts whether a loan should be approved or denied.

  • Updated Aug 27, 2025
  • Jupyter Notebook

A Decision Tree Classifier was implemented to predict personal loan acceptance using a dataset of 5,000 customers. Key features included income, education, mortgage, and credit card usage. The model achieved 97% accuracy, with 92% precision and 76% recall for positive loan predictions, validated using a classification report and confusion matrix.

  • Updated Jun 21, 2025
  • Jupyter Notebook

This a practice project for Classification model with different models like Logistic Regression, Decision Tree Classifier, Random Forest Classifier and Xgboost Classifier. At the end, Logistic Regression gave the best result.

  • Updated Jun 1, 2022
  • Jupyter Notebook

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