| --- |
| 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. |
|
|
