Modern PHP 8.3+ ecommerce platform built on Symfony, Doctrine DBAL, Laminas, 100% vanilla JS. Drop-in replacement for Magento 1 projects with full compatibility. Complete toolchain: Composer/PHPStan plugins, language packs. Enterprise features: automated email marketing, customer segmentation, dynamic categories, passkey/2FA auth and so much more.

An end-to-end data analytics project using SQL, Python, and Power BI to perform customer segmentation

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Customer segmentation project using Python (RFM + KMeans) and Power BI to identify key customer groups and support targeted marketing strategies. Based on real ecommerce data.

Successful forecast a customers projected expenditure throughout their life-time on an eCommerce platform using modern machine learning methods (deep neural networks). Along with investigating customer behaviour like customer retention and segmentation.

A BI project analyzing eCommerce sales data from 2016-2019 to reveal trends in revenue, delivery time, product popularity, returns, and customer segmentation.

This repository contains the solution for an eCommerce transactions analysis assignment. It includes exploratory data analysis (EDA), customer lookalike modeling, and customer segmentation. The project derives business insights, builds a recommendation system, and applies clustering techniques for segmentation.

This project utilizes RFM (Recency, Frequency, Monetary) analysis to perform customer segmentation for an eCommerce dataset. The goal is to group customers based on their purchasing behaviors, which can then inform targeted marketing strategies, improve customer retention, and ultimately enhance profitability.

his repository contains a data science project focused on analyzing eCommerce transactions. It includes Exploratory Data Analysis (EDA), building a Lookalike Model, and Customer Segmentation using KMeans clustering. Key findings include customer base growth, revenue performance, product portfolio balance, and regional market insights.

This project analyzes an eCommerce dataset using EDA for insights, builds a Lookalike Model for customer recommendations, and applies clustering for segmentation. The aim is to improve business strategies by understanding customer behavior, enhancing targeting, and providing data-driven solutions for growth and decision-making.

Project: ecommerce-customer-segmentation

Customer Segmentation and Revenue Analysis for a Gifting Store using Python and Power BI

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Customer segmentation for an e-commerce platform using unsupervised learning and clustering techniques, combining SQL-based data exploration, feature engineering, and model evaluation.

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Using pyspark, Created a customer segmentation using k-means clustering.

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Power BI dashboard analyzing UK-based transactions and customer segmentation (RFM model)

This is my FYP(Final Project)

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Project: Customer Analytics, Customer Segmentation and Customer Lifetime Value for Ecommerce

In this project the customers of an English eCommerce Company are segmented. The data belonging 2010 and 2011 are retrieved from "https://archive.ics.uci.edu/ml/datasets/Online+Retail+II". RFM analysis method is used to segment the customers.

machine learning model project of Ecommerce customer segmentation

This is my second project which Ecommerce customer segmentation.

Final project for data science course. Ecommerce data analysis, customer segmentation, customer value prediction

Comprehensive data science project on eCommerce transactions featuring EDA, customer lookalike modeling, and segmentation.

Ecommerce data analysis project delivering customer segmentation, lookalike modeling, and business insights to improve marketing and customer strategy