A modularized SDK library for Amazon Selling Partner API (fully typed in TypeScript)

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.

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.

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.

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.

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 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.

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 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.

A data analysis project exploring consumer behavior and sales trends through EDA using Python. Includes visualizations and insights derived from retail shopping data.

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.

This repository contains results of the completed tasks for the Quantium Data Analytics Virtual Experience Program by Forage, designed to replicate life in the Retail Analytics and Strategy team at Quantium, using Python.

MobileNetV2-UNet semantic segmentation for Starbucks logo detection - 50ms inference with PyTorch Lightning, binary mask output for mobile deployment

A Data Analysis project performing Exploratory Data Analysis (EDA) on Global Electronics' data to uncover insights that enhance customer satisfaction, optimize operations, and drive business growth.

International electronics retail analysis across 15+ countries using 50K+ sales records | Power BI, SQL, Python, DAX, Interactive Dashboards

RFM customer segmentation analysis of £17.7M retail dataset using K-means clustering and Python

Solution to Quantium Virtual Internships on Forage

Implementation of a d3.js Visual Analytics dashboard for Sales Analysis and Customer Segmentation in Retail

AI-driven retail analytics platform with predictive inventory management, dynamic pricing, and marketing optimization for Walmart Sparkathon 2025

This project is based on supply chain analytics along with demand forecasting and inventory management of the top selling product. Demand forecasting is done by using the prophet time series model. Also, the dashboard consists of all the important insights related to customers, products, orders as well as the forecasting outcomes.

AI-powered real-time people counting system using YOLOv8. Cross-platform desktop app for retail, restaurants, offices, and events. 100% offline, privacy-first.

No description provided.

Retail-RAG: A Python-based Retrieval-Augmented Generation (RAG) system for business insights using OpenAI GPT and FAISS. Ingests retail data, generates embeddings, and enables semantic search for financial, customer, and operational insights. Scalable API layer for real-time data-driven decision-making.

Analyse the customer purchase behaviour to optimize inventory cost

An Excel-based sales performance dashboard analyzing Blinkit’s sales, inventory, and customer ratings to support data-driven business decisions.

A synthetic digital twin of a retail supply chain network, simulating the optimization model annual refresh process used by large retailers (Home Depot, Walmart, Lowe’s, Amazon) to guide long-range supply chain investments and explore cost, service and scenario tradeoffs. [Website is frontend only, DB is stored locally in SQLite]

📈 Forecast weekly and monthly book sales using advanced models like SARIMA, XGBoost, LSTM, and hybrid approaches for accurate retail insights.

This project looks at the sales pattern of a product category in a retail store, using the store’s transaction dataset and identifying customer purchase behavior, to generate insights and recommendations.