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# 💳 Credit Card Fraud Detection — HF Space (Calibrated RF Model)
This is an interactive **Gradio demo** of a calibrated Random Forest model for credit card fraud detection.
The model was trained on the [Kaggle Credit Card Fraud dataset](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud),
and probability calibration ensures reliable decision thresholds for business scenarios.
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## 🚀 How to Use
1. **Upload your CSV** with transaction rows.
- Required columns: `V1` … `V28`, `Amount`
- Either include engineered features, or just add `Time` (seconds from start)
→ the app will automatically derive:
- `_log_amount`
- `Hour_from_start_mod24`
- `is_night_proxy`
- `is_business_hours_proxy`
2. **Adjust the decision threshold** with the slider.
- Default is set to the validation threshold for **Precision ≥90%** (`≈0.712`).
- Move it left/right to trade off between precision and recall.
3. **Preview results** (first 50 rows) or enable **Return all rows** for the full file.
- Each row includes:
- `Fraud_Probability`
- `Prediction (0 = normal, 1 = fraud)`
4. **Download results** as `predictions.csv`.
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## 🧪 Try with Example Data
You don’t need to bring your own data to test the app!
Just click **Use Example** inside the app, and it will load the included `example_transactions.csv`.
This file mimics the required structure:
- 60 transactions
- Columns: `V1..V28`, `Amount`, `Time`
- Probabilities + predictions are generated live with the same calibrated RF model.
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## 📊 Notes
- The model is calibrated with **Isotonic Regression** for probability reliability.
- Default threshold corresponds to **Precision ≥90%**, aligning with fraud detection team priorities.
- For production use, re-tune thresholds regularly as data drift changes prevalence and costs.
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## 🔗 Related
- [Model repo on Hugging Face Hub](https://huggingface.co/TarekMasryo/CreditCard-fraud-detection-ML)
- [Original Kaggle dataset](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)
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