A fully interactive data storytelling dashboard for e-commerce analytics. Built with Python, Streamlit, and Plotly, it transforms transactional data into actionable insights through KPIs, cohort retention, RFM segmentation, and global visualizations, perfect for analysts and data scientists.

Free AI agent skill for searching and analyzing 10M+ ecommerce stores — GMV, tech stack, contacts, and competitor data, powered by EcCompass.AI.

Amazon product data extraction tool

AI agent for answering SQL-based questions using LLaMA and FastAPI

Full-stack sales analytics for 80k+ Flipkart listings. Features a Python-to-SQL ETL pipeline and interactive Power BI dashboards for inventory and seller auditing.

AI-powered multi-brand ad management platform. Track real profit across Shopify + Meta, Google, TikTok & Snapchat ads in one dashboard. Automated ROAS analysis, COGS-aware P&L, Telegram alerts, and AI recommendations. Built with Next.js 15, Drizzle ORM & Neon PostgreSQL.

End-to-end analytics system identifying high-risk sellers and revenue exposure in an e-commerce marketplace using Excel, PostgreSQL, and Power BI.

🧠 A Streamlit web app that predicts Amazon sales amounts using a Multilayer Perceptron (MLP) model. Features real-time input, cleaned data, and a trained MLPRegressor with encoding and scaling pipelines.

Samsung AI Semi-Project

End-to-end data analyst project analyzing e-commerce funnel performance, revenue leakage, delivery delays, and seller performance using Python, SQL, and Tableau on the OLIST dataset.

Interactive Power BI dashboard analyzing Brazilian e-commerce performance across 27 states

Global Amazon sales analytics project using Power BI to analyze 50K+ ecommerce records and uncover sales trends, market performance, and category insights.

End-to-end E-commerce Data Analysis using PySpark: Customer Segmentation (RFM), Market Insights, and Logistics Performance Dashboard.

An interactive interface for performing CRUD operations (Create, Read, Update, Delete) on a MySQL database related to Zomato data.

Interactive Power BI dashboard analyzing e-commerce sales, revenue trends, and customer insights using Excel and Power Query.

🛍️ Analyze retail data with our platform, scraping insights from major retailers, processing with ETL, and delivering interactive analytics via Streamlit.

End-to-end Customer Lifetime Value (CLV) Prediction & Retention Analytics System built with Python, XGBoost, and Streamlit — includes RFM segmentation, cohort analysis, persona insights, model monitoring, drift detection, logs analytics, and automated executive summary reporting.

This project aims to analyze e-commerce data to derive meaningful insights about customer behavior, sales trends, and product performance. We utilize Python, MySQL, and various data visualization libraries to perform the analysis.

Q2 sales and customer behaviour analysis identifying £70k-£95k annual revenue opportunities through delivery optimisation and discount strategy refinement.

📊 Analyze Amazon sales data with an interactive Power BI dashboard to uncover insights on performance, trends, and product effectiveness.