This project builds an Intelligent Customer Segmentation System using Unsupervised Machine Learning to discover hidden behavioral patterns and group customers into meaningful clusters.

This repository contains a data-driven customer segmentation analysis for the SmartCart e-commerce platform. Using Python and machine learning techniques, this project identifies distinct customer personas based on demographic profiles, purchasing behaviors, and engagement metrics.

Unsupervised machine learning project clustering customers using K-Means and Agglomerative clustering based on customer information.

SmartCart Customer Clustering System uses unsupervised machine learning to segment 2,240 customers based on demographics, spending behavior, purchase frequency, engagement, and recency. It uncovers meaningful customer groups to enable personalized marketing, improve retention, detect churn early, and support data-driven decisions.

A Smart Cart Clustering System is a technology-driven system used in modern retail environments where shopping carts are integrated with sensors, RFID, or IoT devices to track customer behavior and purchase patterns. The system applies clustering algorithms (like K-means, hierarchical.) to group customers or products based on similarities

Project Uses ML algorithms to segregate different types of customers and their buying needs in an Online E- Commerce Platform.

SmartCart is an intelligent customer segmentation system built using unsupervised machine learning.

SmartCart Customer Segmentation: An end-to-end unsupervised machine learning pipeline using PCA, K-Means, and Agglomerative Clustering to identify distinct customer purchasing personas based on demographic and behavioral data.

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SmartCart is a growing e-commerce platform serving customers across multiple countries .The company collected extensive customer data consisting of 2240 customer records and 22 attributes including demographics,purchase behaviour website activity and response

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Built a customer segmentation model using KMeans and Agglomerative Clustering, identifying 4 distinct customer groups and deriving actionable marketing strategies based on income, spending behavior, and engagement patterns.

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SmartCart Clustering System is a Machine Learning project that segments customers into different groups based on their purchasing behavior. The goal is to help businesses understand customer patterns and create targeted marketing strategies.

Unsupervised machine learning project for e-commerce customer segmentation using K-Means clustering. Includes data preprocessing, feature scaling, EDA, and cluster evaluation with Elbow Method and Silhouette Score to enable data-driven marketing strategies.

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SmartCart Clustering System is an Unsupervised Machine Learning project designed to perform customer segmentation using clustering algorithms. The goal of this project is to analyze customer behavior and group similar customers together based on purchasing patterns and demographic features.

SmartCart - An E-commerce customer segmentation system.SmartCart aims to build an intelligent customer segmentation system using unsupervised machine learning. The system will analyse historical customer transaction data and group customers into meaningful clusters based on purchasing behaviour, engagement levels, and loyalty indicators.

A Machine Learning project built with Python and Scikit-learn to perform customer/product segmentation using clustering techniques. Includes EDA with Matplotlib and Seaborn.

ML project

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Customer segmentation using K-Means and Agglomerative Hierarchical Clustering with extensive data visualization and anomaly detection to uncover behavioral customer patterns and insights.