End-to-end analytics suite for Blinkit, Swiggy, and JioMart — 100k+ orders analyzed to forecast demand, optimize SLAs, and reduce churn (+37% growth impact)

Quick-commerce delivers in 10 minutes. But the customer spends 15 minutes deciding what to order. The bottleneck isn't the rider — it's the shopping itself.

Score your quick commerce cities by ad performance. Know where to scale, hold, or cut budget.

This is the server side code for a real life quick commerce application clone

Platform connecting local shops for ultra fast cheap delivery... Created at https://coslynx.com

Business analytics & data visualization project analyzing Blinkit’s quick commerce model-delivery speed, urgency marketing, customer behavior, and revenue insights using Tableau.

AI-powered quick commerce intelligence system that tracks products, pricing, and availability across Blinkit, Zepto, and Instamart.

Built an executive-level Quick Commerce War Room using Python, Pandas, Power BI, and DAX to analyze 500K+ orders, customer behavior, product performance, and delivery operations.

Power BI dashboard for analyzing Blinkit sales and quick commerce trends

AI-powered system for optimizing inventory, demand forecasting, and supply chain in quick commerce dark stores

End-to-end quick commerce analytics project leveraging Excel, SQL, Python, and Power BI for sales analysis, customer insights, and business performance monitoring.

🏗️ System architecture study comparing e-commerce vs quick-commerce — focusing on backend architecture, data consistency, scalability, and operational complexity.

Fully-featured e-commerce platform designed for performance and scalability. The backend is powered by Django Rest Framework (DRF), ensuring secure and efficient API endpoints, while the frontend is built with React to deliver a seamless, responsive user experience.

End-to-end Business Intelligence project for identifying expansion-ready districts using Python, SQL, and Power BI.

End-to-end quick commerce analytics comparing Zepto vs Blinkit — SQL, Python (t-test, chi-square, K-Means, RFM, SPC, ARIMA), Excel, Power BI 7-zone war room

DailyDelish: Fresh Produce, Your Way

Quick-Commerce delivery analytics using Python — EDA, hypothesis testing, ML classification, clustering, and time series forecasting on 591K records.

Full-stack quick-commerce grocery app — Flutter mobile client + Node.js/Express backend with phone OTP auth, live order tracking via SSE, and admin dashboard

Excel-based analysis of 15-minute delivery SLA performance for Zepto & Instamart