| --- |
| title: Bank Churn Pro Demo |
| emoji: 🏦 |
| colorFrom: blue |
| colorTo: indigo |
| sdk: docker |
| app_port: 7860 |
| pinned: false |
| --- |
| |
| # Bank Churn Pro Demo |
|
|
| A Hugging Face Docker Space for bank customer churn analysis with: |
|
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| - Full-screen Bank Churn background UI |
| - Pipeline Step 1/2/3 execution log |
| - Feature importance chart |
| - Churn probability gauge |
| - CSV batch prediction |
| - SHAP explainability |
|
|
| ## Included files |
|
|
| - `app.py` - Gradio app |
| - `scripts/pipeline.py` - training / artifact generation pipeline |
| - `data/bankChurn.csv` - sample dataset |
| - `assets/Bank Churn.png` - background image |
|
|
| ## Expected workflow |
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| 1. Open the Space |
| 2. Go to **Pipeline** and click **Run Pipeline** |
| 3. Wait for the 3-step pipeline to finish |
| 4. Use **Single Prediction**, **Batch CSV**, and **Explainability** tabs |
|
|
