
Model type	XGBoost (XGBClassifier)
Task	Binary classification — default prediction
Dataset	UCI Credit Card Dataset via `imodels/credit-card`
Training samples	24,000
Test samples	6,000
ROC-AUC	0.783
Accuracy	82%

📊 Performance

Metric	Score
ROC-AUC	0.783
Accuracy	0.82
Precision (non-default)	0.84
Recall (non-default)	0.95
Precision (default)	0.66
Recall (default)	0.36

🧠 Features Used

Feature	Description
`limit_bal`	Credit limit amount
`age`	Age of the applicant
`pay_0` to `pay_6`	Repayment status for past 6 months
`bill_amt1` to `bill_amt6`	Bill statement amounts
`pay_amt1` to `pay_amt6`	Payment amounts
`sex`	Gender (one-hot encoded)
`education`	Education level (one-hot encoded)
`marriage`	Marital status (one-hot encoded)

🚀 How to Use

Load and run inference

python

import joblib
import json
import pandas as pd
from huggingface_hub import hf_hub_download

# Load model and features
model_path = hf_hub_download(repo_id="shrey1905/credit-default-model", filename="model.joblib")
features_path = hf_hub_download(repo_id="shrey1905/credit-default-model", filename="feature_names.json")

model = joblib.load(model_path)
with open(features_path) as f:
    feature_names = json.load(f)

# Build input (all unused features set to 0)
input_dict = {f: 0 for f in feature_names}
input_dict["limit_bal"] = 50000
input_dict["age"] = 35
input_dict["pay_0"] = 0
input_dict["bill_amt1"] = 5000
input_dict["pay_amt1"] = 1000
input_dict["sex:1"] = 1
input_dict["education:1"] = 1
input_dict["marriage:1"] = 1

df = pd.DataFrame([input_dict])[feature_names]
prob = model.predict_proba(df)[0][1]
print(f"Default probability: {prob:.1%}")

🖥️ Live Demo

Try the model interactively here: 👉 https://huggingface.co/spaces/shrey1905/credit-default-app

⚠️ Limitations

Trained on Taiwan credit card data from 2005 — may not generalise to other geographies or time periods
Class imbalance (~22% defaulters) means recall on defaulters is moderate (36%)
Not intended for real-world credit decisions without further validation

🛠️ Training

python

from xgboost import XGBClassifier

model = XGBClassifier(
    n_estimators=200,
    max_depth=4,
    learning_rate=0.05,
    eval_metric="logloss",
    random_state=42
)

👤 Author

Built by @shrey1905