{"repo":"aimaster-dev/default_loan_prediction","free":true,"listed":false,"github":"https://github.com/aimaster-dev/default_loan_prediction","clone":"git clone https://github.com/aimaster-dev/default_loan_prediction.git","description":"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.","language":"JavaScript","stars":12,"topics":["flask","vuejs","fintech","loan-prediction","lightgbm","automation","banking-applications","classification","credit-risk","explainability"],"license":null,"category":"workflow-automation","readme_excerpt":"🤖 Loan Default Prediction – Automating Bank Credit Processes Using AI This project applies AI and machine learning to predict whether a customer is likely to default on a loan, enabling banks to make informed and automated credit decisions. --- 📊 Problem Statement Banks often struggle to accurately assess credit risk and loan default probability. This project automates that process by using classification models to predict default based on applicant data. --- 🧠 Key Features - Predicts loan defaults using historical banking data - Compares multiple ML algorithms (Logistic Regression, Random Forest, XGBoost, etc.) - Evaluates models via ROC-AUC, F1 score, Precision, and Recall - Provides explainability with SHAP (SHapley Additive exPlanations) - Visualization of feature importance and model decisions - End-to-end pipeline from data preprocessing to model interpretation --- 🧰 Tech Stack - Python - scikit-learn - XGBoost - SHAP - pandas , matplotlib , seaborn --- 🛠️ Setup Instructions 1. Clone the Repository 2. Install Dependencies 3. Run the Notebook Open loan default prediction.ipynb in Jupyter or run it using: --- 📈 Model Evaluation Best Model: XGBoost ROC-AUC: High discriminative power SHAP Values: Used to interpret individual predictions and global feature impact --- 📌 Highlights Automated model selection and tuning Transparent credit risk scoring using SHAP Business-focused evaluation for banking applications --- 📎 Resources 📄 Project Report (PDF) --- 📜 License MI","default_branch":null,"files":null,"tree":[],"storefront":"/r/aimaster-dev","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/aimaster-dev/default_loan_prediction/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}