Demand Forecasting & Inventory Optimization — Cut Simulated Stockouts from 20% to 11% The Problem A recurring pain point for retail and e-commerce businesses: how much stock to hold per product, and when to reorder — without either running out (lost sales) or over-ordering (wasted capital). I built this project end-to-end on a real 2-year, 1M+ row UK online retail transaction dataset to show how that decision can be made with data instead of guesswork. What I Did Cleaned 1M+ raw transactions: resolved cancellations, removed non-product entries, and separated one-off wholesale bulk orders from genuine recurring retail demand — a distinction that changed the shape of the demand data significantly. Classified 1,760 products by revenue impact (ABC/Pareto analysis) and demand behavior (smooth, erratic, intermittent, lumpy — using the Syntetos-Boylan method from supply-chain statistics). Backtested 9+ forecasting models — from simple moving averages to Croston's Method (for intermittent demand) to a global LightGBM model with quantile regression — using proper rolling-origin backtesting across 3 time periods, including the toughest case: the Q4 holiday demand surge. Built a full inventory policy: safety stock and reorder points using both classical and non-parametric (quantile-based) methods, simulated against real held-out demand. Delivered an interactive dashboard (built in HTML/JS, deployed live) so a non-technical stakeholder can explore any product's forecast, view inventory recommendations, and see model performance — no spreadsheet required. Key Results Identified that 21.6% of products drive 80% of revenue (classic Pareto effect) — informing where forecasting effort should focus. Found that unrepeated wholesale bulk orders were distorting demand patterns for a subset of products, and built a cleaning method to separate wholesale noise from real retail demand. Compared machine learning (LightGBM) against classical time-series methods honestly — reporting where ML wins, where it doesn't, and why (a well-tuned moving average performed comparably at the individual-product level). Simulated the recommended inventory policy against real Q4 demand: cut stockout weeks from ~20% (naive rule) to ~11%, while explicitly weighing that against a fair (matched-service-level) comparison to a classical method — an honest, non-oversold conclusion.

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A/B Testing & Causal Inference — 64,000-Customer Email Campaign Analysis (Hillstrom Dataset) A full randomized-experiment analysis, built to show the difference between running a statistical test and running one correctly — including catching and fixing my own methodology bug mid-project. Result: A clear, statistically defensible recommendation (send the Men's email campaign to the full customer base) with honestly quantified conditions under which that recommendation would change.

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Marketing Campaign ROI Dashboard — Budget Reallocation from Real Facebook Ad Data Built a full-cycle marketing analytics project: took a real anonymized company's Facebook ad campaign data (1,143 ads, ~$58.7K spend) and turned it into a budget reallocation recommendation backed by statistical testing, not just intuition.

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Predicting Repeat Customers for a 96K-Order E-Commerce Marketplace

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Rahul Singh

Data Analyst | Statistics, Python, SQL & Power BI