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README.md

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+---
+tags:
+- tabular-classification
+- xgboost
+- scikit-learn
+- loan-default
+---
+
+# Loan Default Classifier
+
+Binary classifier predicting whether a loan applicant will default, mirrored here from the
+MLflow Model Registry of the source project (`loan-default-classifier`, best model: **xgboost**).
+
+## Test set performance
+
+| Model | Accuracy | Precision | Recall | F1 | ROC-AUC | PR-AUC |
+|---|---|---|---|---|---|---|
+| logistic_regression | 0.672 | 0.214 | 0.680 | 0.325 | 0.739 | 0.313 |
+| random_forest | 0.856 | 0.358 | 0.309 | 0.332 | 0.740 | 0.310 |
+| xgboost | 0.687 | 0.221 | 0.671 | 0.333 | 0.746 | 0.324 |
+
+## Usage
+
+`model.pkl` is a scikit-learn `Pipeline` (preprocessing + classifier) saved with `joblib`. It expects
+a single-row DataFrame with these raw feature columns:
+
+`Age, Income, LoanAmount, CreditScore, MonthsEmployed, NumCreditLines, InterestRate, LoanTerm, DTIRatio, Education, EmploymentType, MaritalStatus, HasMortgage, HasDependents, LoanPurpose, HasCoSigner`
+
+```python
+import joblib
+import pandas as pd
+
+pipeline = joblib.load("model.pkl")
+row = pd.DataFrame([{...}], columns=['Age', 'Income', 'LoanAmount', 'CreditScore', 'MonthsEmployed', 'NumCreditLines', 'InterestRate', 'LoanTerm', 'DTIRatio', 'Education', 'EmploymentType', 'MaritalStatus', 'HasMortgage', 'HasDependents', 'LoanPurpose', 'HasCoSigner'])
+probability = pipeline.predict_proba(row)[0, 1]
+```
+
+Trained as part of an end-to-end MLOps pipeline (DVC + MLflow + FastAPI + Docker + GitHub Actions).
+Source repository: https://github.com/Yashwanth-R19/loan-default-mlops