Streamlit Application for ABC Analysis & Product Segmentation
-
Updated
Feb 7, 2025 - Python
Streamlit Application for ABC Analysis & Product Segmentation
The original dataset is a year's worth of electronics sales transactions. It has around 185K records and 11 attributes. Business Objective: Identify the products which generated 80% of profit.
A Streamlit app for supply chain data analytics, forecasting, inventory optimization, customer segmentation, and statistical testing. Built using Python, machine learning, and interactive visualisations.
This repository contains an end-to-end exploratory data analysis of transactional data from a UK-based online retail store covering the period from December 2010 to November 2011. The goal is to uncover sales trends, customer behavior, and product performance, and to provide actionable recommendations that can guide strategic business decisions.
This project focuses on analyzing product segmentation and optimizing discounts for the upcoming seasons to maximize sales and revenue.
Add a description, image, and links to the product-segmentation topic page so that developers can more easily learn about it.
To associate your repository with the product-segmentation topic, visit your repo's landing page and select "manage topics."