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l train and evaluate multiple time-series forecasting models using the Store Item Demand Forecasting Challenge dataset from Kaggle. This dataset has 10 different stores and each store has 50 items, i.e. total of 500 daily level time series data for five years (2013–2017).

  • Updated Jun 13, 2024
  • Jupyter Notebook

Healthcare demand forecasting and staffing decision platform (PoC): a 13-model benchmark (baselines, SARIMAX, Prophet, global gradient boosting, Nixtla Stats/ML/Neural, Chronos), conformal prediction intervals, rolling-origin backtesting, a costed staffing layer, batch pipeline, FastAPI serving, monitoring and responsible-ML docs.

  • Updated Sep 3, 2026
  • Jupyter Notebook

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