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.

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.

🧠 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 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.

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

Analysis of an E-Commerce customer database that lists purchases made by over 4000 customers over a period of one year, developed a model that allows to anticipate the purchases that will be made by a new customer, during the following year, from its first purchase.

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

Cleaned and visualized 20K+ messy sales records for gaming products, building interactive dashboards in Tableau to reveal insights by product, region, and channel.

Monthly customer analysis of the TheLook eCommerce dataset (BigQuery + Looker Studio)

Extract detailed company data from Storeleads

This repository contains a Power BI dashboard project focused on Amazon Sales Analytics. It provides insights into: Total sales and profit trends, Product category performance , Regional sales analysis, Customer segment insights .

End-to-end CRM analytics using cleaned eCommerce datasets to identify retention drivers, analyze funnels, and predict repurchase. Includes automated Python pipelines, logistic and linear modeling, and interactive Power BI dashboards for actionable business insights.

extract amazon product reviews efficiently

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

An end-to-end data pipeline using the Brazilian E-Commerce dataset. Data is extracted from CSV and PostgreSQL, transformed with Python and dbt, and visualized in Power BI. Airflow orchestrates and automates the ETL process to deliver reliable insights on sales performance, customer behavior, and product trends.

Web scraping and analysis of sneaker listings from Amazon.sg to uncover pricing, brand strategy, and customer rating insights.

End-to-end Amazon E-commerce Sales Analytics Dashboard built using Power BI, DAX, and Excel. Includes raw dataset, data cleaning workflow, DAX measures, interactive sales insights, courier performance tracking, category analysis, and state-wise business trends.

Advanced ML case study for e-commerce customer rating prediction. Compares four linear regression models across different feature sets and training data sizes. Implements correlation-based feature selection and robust evaluation using MSE, RMSE, R² metrics. Identifies linear model limitations.

Professional e-commerce analytics with interactive charts and responsive design. It provides real-time insights into customer behavior, sales performance, and business metrics through interactive visualizations.