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README_space.md

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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.
+
+---
+
+## 🚀 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`.
+
+---
+
+## 🧪 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.
+
+---
+
+## 📊 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.
+
+---
+
+## 🔗 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)