Portfolio of reproducible data-science projects (forecasting + NLP) on synthetic retail datasets. Notebooks, figures, and READMEs included.

This Jupyter Notebook implements Bayesian modeling techniques to fit a posterior distribution and forecast demand for an e-commerce company.

Urban Mobility Data Analysis in NYC Uber rides | Time Series Analysis, Data Visualization, Correlation Analysis, Feature Engineering | Exploring the impact of location using Python (Pandas, NumPy, Matplotlib, Seaborn), Jupyter Notebook.

Uber Demand Analysis in NYC | Time Series Analysis, Data Visualization, Correlation Analysis, Feature Engineering | Exploring the impact of weather, holidays, and location using Python (Pandas, NumPy, Matplotlib, Seaborn), Jupyter Notebook.

Retail-Forecast — tools, notebooks, and models for demand forecasting and time-series analysis 📈🧠 Designed for retail use-cases (sales, promotions, inventory) with reproducible pipelines and experiments 🛒🔁