retail-analytics

Personal project: where shoppers look, estimated from an ordinary security camera and open source AI (YOLO11, Gaze-LLE, Depth Anything V2)

2026 Gadget Sales Analytics: Brand Profitability & Consumer Demographics Optimization

Automated Double Machine Learning (DoubleML + AutoML) pipeline for causal inference — estimate incremental impact, CATE & ROI of promotions, pricing and media.

Predict the profitability of potential coffee shop locations using SQL and Python. Combines data engineering with feature-rich regression modeling, visual analytics, and business insights to support data-driven site selection and retail decision-making.

AI-powered retail intelligence platform that converts CCTV footage into footfall analytics, heatmaps, queue intelligence, conversion funnels, anomaly detection, and sales insights using YOLOv8, ByteTrack, FastAPI, and Docker.

Python project for Market Basket Analysis. Generates synthetic retail transactions, mines frequent itemsets using Apriori & FP-Growth, derives association rules, and outputs CSVs + visualizations. Portfolio-ready example demonstrating data science methods for uncovering product co-purchase patterns.

A complete exploratory data analysis (EDA) and forecasting project focused on retail sales data. The project identifies key sales patterns, seasonal trends, and builds predictive models to forecast future demand at the item-store level.

Analyze retail sales data using SQL and Python. Build a SQLite database from CSV, run SQL queries for key KPIs (revenue, top products, AOV, trends), and visualize results with Matplotlib. A portfolio-ready project demonstrating SQL + data analytics + reporting automation.

A powerful eBay scraper built with Scrapy that extracts product listings, prices, seller data, and auction information from eBay marketplaces worldwide. Features anti-bot protection, price intelligence, multi-format export (CSV/JSON), and global eBay site support.

This is a real-world business use case, often tackled with data analysis, machine learning, and geospatial visualization. working on a store placement prediction project where the goal is to visualize and predict ideal locations for placing a new store, using a map generated on your system.

Public Snowflake and dbt pipeline transforming Blinkit retail sales data into staging and mart analytics models.

A real-time Retail Shelf Monitoring System using computer vision and machine learning. Detects out-of-stock products, misplaced items, and ensures planogram compliance through intelligent video analytics and a desktop management interface.

Architected a high-performance predictive pipeline processing 15 Million transactions. Optimized memory by 70% via custom downcasting and implemented Tweedie-LightGBM to solve zero-inflation in retail demand. Delivered a 28-day future forecast for Walmart inventory with high-precision RMSE of 2.20.

A real-time bidirectional people counting and foot-traffic analytics system powered by YOLOv11 and OpenCV. Features multi-object tracking (MOT), dual-polygon region-of-interest (ROI) logic for entry/exit detection, and automated video reporting. Perfect for retail analytics and smart occupancy monitoring.

End-to-end sales forecasting and XAI pipeline for weather-sensitive retail demand using PostgreSQL, dbt, FastAPI, and Streamlit.

Retail analytics in Python: RFM, HML and threshold segmentation, gain/loss, cross-shop, cohort retention, revenue trees and market basket analysis, on pandas or in your database via Ibis

Avocado price forecasting using Facebook Prophet on retail avocado sales data, with analysis, visualization, and 2019 price prediction.

End-to-end café inventory project: clean transaction data, build daily item-level demand series, backtest strong baseline forecasters, generate next-30-day demand forecasts, convert forecasts into safety stock + reorder points, and validate policies with Monte Carlo stockout-risk simulations, wrapped in a Streamlit dashboard.

A retail analytics capstone that converts transactions into a calendar intelligence system. It quantifies day-of-week and monthly seasonality, builds a baseline expected revenue model, detects event-like spike days using robust residual z-scores, and explains spikes via transactions, units, AOV, and category mix, with a Streamlit dashboard+exports.

AI-powered retail analytics platform that uses computer vision to track customer behavior, optimize store layouts, manage inventory, and provide personalized shopping recommendations using transformer architectures.