AI Loan Approval Predictor
Overview
This Logistic Regression model achieves 91.14% accuracy with a ROC-AUC score of 0.90, and was trained on over 58,000 loan applications.
Built using Python scikit-learn for model development, FastAPI for the backend service.
Interactive Demo
Try the loan predictor with different applicant profiles. The model analyzes multiple factors to provide real-time approval predictions.
Personal Information
Loan Information
Model Performance
91.14%
Accuracy
0.90
ROC-AUC Score
58K+
Training Samples
The model demonstrates strong predictive performance with high accuracy and ROC-AUC score.
Technical Implementation
Data Science Pipeline
- Feature engineering with one-hot encoding for categorical variables
- StandardScaler normalization for numeric features
- Logistic regression using L2 regularization (scikit-learn default)