These projects translate the same research workflow that drives my academic work—estimate behavioral response, simulate counterfactuals, and evaluate decision rules—into deployable tools for pricing, market analytics, and regional decision support. The core logic is identical to the one used in regional trade and market-integration models; the unit of analysis simply shifts from regions to firms or places.

Counterfactual Decision Tools

Pricing and Promotion Decision Engine (2025–Present)

• Live app: pricing-promotion-decision-engine.streamlit.app
• Code: github.com/Li-Shen-Clark/pricing-promotion-decision-engine
• Stack: Python (pandas, statsmodels, scikit-learn), Streamlit, pytest, Git/GitHub
• Narrative role: Industry translation of the estimate–simulate–optimize workflow used in my regional trade research.

An end-to-end demand-estimation and decision-support tool built on 6.6M rows of Dominick’s Finer Foods scanner data (cleaned panel of 486 UPCs, 93 stores, 366 weeks). The workflow is the same one I use in my academic work, applied to firm-level pricing instead of regional GDP.

• Estimate behavioral response: log-log fixed-effects demand model with Duan smearing retransformation to reduce log-to-level retransformation bias.
• Simulate counterfactuals: price and promotion counterfactual simulations over the estimated demand system.
• Optimize under constraints: constrained profit optimization under cost, inventory, and margin limits; A/B validation plans with power analysis before any candidate action ships.
• Ship: deployed as an interactive Streamlit application backed by a modular Python codebase, a pytest test suite, reproducible requirements, and auto-deploy from GitHub.

Machine Learning Competitions

Kaggle Silver Medal — Home Credit Credit Risk Model Stability (Feb–May 2024)

• Stack: Python, Polars, scikit-learn, LightGBM, CatBoost
• Role: Statistical modeling and co-developer

Competition focused on model stability over time (Gini Stability metric) rather than raw AUC — a realistic production concern for credit-risk models that retrain on drifting data.

• Co-developed a Polars lazy-execution ETL pipeline for multi-depth relational data, enabling fast iteration on feature definitions across parent-child tables.
• Designed aggregation features (max, last, mean) capturing borrower behavioral trends; these were adopted across the team codebase.
• Optimized directly for the headline Gini Stability metric to prevent performance drift over time; implemented correlation-based feature grouping (Pearson > 0.8) for multicollinearity control and type-downcasting memory optimization (over 50% reduction).

Kaggle Bronze Medal — ICR Identifying Age-Related Conditions (May–Aug 2023)

• Stack: Python, TabPFN, XGBoost, imbalanced-learn
• Role: ML researcher and co-developer

Competition on a small, severely class-imbalanced tabular dataset with auxiliary metadata (Greek) hinting at latent subgroup structure.

• Designed a stratified resampling strategy using the Greek metadata (RandomOverSampler on Alpha-defined latent subgroups) to preserve population structure under severe class imbalance.
• Co-developed a voting ensemble combining parametric (XGBoost) and non-parametric (TabPFN) models under a custom Balanced Log Loss metric.
• Calibrated decision thresholds to prioritize recall in a severely imbalanced classification setting.

Geospatial Measurement Infrastructure

China Population Density Atlas (2022–Present)

• Live app: pop-density-spatial.streamlit.app
• Stack: Python (GeoPandas, rasterio, NumPy), Streamlit
• Companion paper: Population Agglomeration and Spatial Reallocation in China (SSRN preprint)
• Narrative role: Supporting capability — demonstrates geospatial data construction, visual analytics, and regional measurement infrastructure that underpin my regional-economics research.

Interactive geospatial visualization of China’s population geography built from 19 years of 5 km-resolution population-density rasters (2002–2020) harmonized into an annual panel.

• Processed WorldPop rasters covering the full territory of China into grid-cell and province-level summaries.
• Surfaced documented patterns from the underlying research: rising spatial Gini, expansion of dense urban corridors, and a 17 km southeastward shift in the population-weighted centroid.
• Deployed in 2025 for map-based exploration of density, agglomeration, and urban-corridor metrics at multiple spatial scales.

Toolkit

• Programming: Python (pandas, NumPy, scikit-learn, statsmodels, GeoPandas, Polars), SQL, R, MATLAB; Git/GitHub, pytest.
• Machine learning: Gradient boosting (XGBoost, LightGBM, CatBoost), cross-validation, feature engineering, model stacking, regularization.
• Deployment: Streamlit (multiple live apps), reproducible requirements, auto-deploy from GitHub.
• Data at scale: Multi-million-row panels, high-frequency price data, geospatial rasters, multi-regional input-output tables.