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workforce-intelligence-platform

An HR analytics platform that predicts employee attrition with lead time for intervention and identifies flight risk factors.

Running the dashboard

The Streamlit app (dashboard.py) reads pickled models and preprocessed data from an artifacts/ directory. That directory is generated by the final cell of project-08.ipynb and is git-ignored.

  1. Install dependencies (Python 3.11+ recommended):

    pip install -r requirements.txt

    On macOS, XGBoost needs libomp:

    brew install libomp

  2. Generate the artifacts — open project-08.ipynb and run all cells to the end. The last cell (Part 6: Export Artifacts for Dashboard) writes about a dozen files into ./artifacts/.

  3. Launch the dashboard from the repo root:

    streamlit run dashboard.py

    The app opens at http://localhost:8501 with four tabs:

    • Overview — headline attrition stats, department/role breakdowns, and LASSO Logistic Regression test metrics.
    • Risk Explorer — per-employee Cox survival forecast + SHAP explanation.
    • Survival Curves — Kaplan-Meier comparisons with log-rank significance.
    • Fairness Audit — parity, calibration, and C-index gaps across protected groups.

Re-run the notebook's export cell any time the models change — the dashboard will pick up the new artifacts on the next reload (R in the Streamlit UI).