dworsleytonks/credit-scoring-xgb

Credit Scoring XGBoost

Summary

A baseline XGBoost model for binary credit risk classification trained on the Home Credit dataset. The model is intended for experimentation and educational use, not production.

Model Details

• —Model type: XGBoost classifier
• —Task: Binary classification (default risk)
• —Input: Tabular features engineered from Home Credit raw tables
• —Output: Probability of default
• —Artifact: credit_scoring_xgb.pkl

Intended Use

• —Demonstrate a credit scoring pipeline with MLflow tracking
• —Provide a lightweight baseline for experimentation

Limitations

• —Not validated for real-world credit decisions
• —No fairness or regulatory compliance audit
• —Performance depends on data preprocessing used in the training pipeline

How to Use

Load the artifact locally with joblib:

import joblib
model = joblib.load("credit_scoring_xgb.pkl")
preds = model.predict_proba(X)[:, 1]

Input Expectations

• —The model expects the same feature engineering used in the training pipeline.
• —See the project notebooks for the exact preprocessing steps and column names.

Reproducibility

• —Training code and experiments are tracked in the main project repo:
• —src/train.py
• —notebooks/01_data_preparation.ipynb
• —notebooks/03_model_comparison.ipynb

Training Data

Home Credit (public dataset). CSVs are tracked via Git LFS in the project repo.

Metrics

See the project repo notebooks for evaluation details and MLflow logs.