Add dataset card

README.md

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+---
+license: mit
+language:
+- en
+task_categories:
+- tabular-classification
+- other
+tags:
+- churn-prediction
+- customer-retention
+- survival-analysis
+- milp
+---
+
+# Churn Predict — Data
+
+Companion dataset for the [`churn_predict`](https://github.com/bravo2024/churn_predict) repository
+(Customer Churn Prediction & Retention-Budget Optimization).
+
+## Files
+
+| Path | Size | Description |
+|---|---|---|
+| `data/raw/ordered_open_ecommerce.parquet` | 150 MB | Raw Open E-Commerce 1.0 purchase records (1,761,259 rows, 2018-01 .. 2023-03). Berke et al. 2024. |
+| `models/artifacts.pkl` | 197 MB | Fitted churn models, SHAP values, survival fits, and MILP results consumed by the Streamlit dashboard. |
+
+## Why on the Hub
+
+Both files exceed GitHub's 100 MB per-file hard limit, so they are hosted here and fetched at
+runtime by `dashboard.py` and `build_dataset.py` (via `huggingface_hub.hf_hub_download`).
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+
+artifacts_path = hf_hub_download(
+    repo_id="vivekkopthsd/churn-predict-data",
+    filename="models/artifacts.pkl",
+)
+```
+
+## Source and License
+
+- **Open E-Commerce 1.0** — Berke, A., Calacci, D., Mahari, R., Yabe, T., Larson, K. and Pentland, S.
+  (2024). *Open e-commerce 1.0, five years of crowdsourced U.S. Amazon purchase histories with user
+  demographics*. Scientific Data, 11. DOI 10.1038/s41597-024-03329-6. MIT (ungated).
+- `models/artifacts.pkl` — generated by `train_models.py` (all random seeds 42).
+- Ethics: only benign demographics and Amazon-usage survey items; sensitive survey items excluded.