Project - Creating customer segments | Unsupervised learning | Python | PCA | Gaussian Mixture Model

Project explores the transaction history of an online household goods store through detailed data analysis, visualizations, and statistical hypothesis testing, offering valuable insights into purchase trends, customer behavior, and strategic product decisions.

Using fuzzy c-means and k-means to analyze customer personality data

A complete data mining pipeline for supply chain and sales analysis, combining exploratory data analysis, predictive modeling, and optimization to generate actionable business insights.

EDA and customer segmentation with RFM analysis on hacker earth dataset. https://www.hackerearth.com/challenge/hiring/LMG-analytics-data-science-hiring-challenge

E-Commerce Customer Segmentation using k-means clustering

Data Mining and Wrangling Mini Project 4 - September 12, 2021

Customer segmentation analysis using unsupervised learning on German demographics data (Bertelsmann Arvato Analytics). The project applies data preprocessing, PCA for dimensionality reduction, and KMeans clustering to identify customer groups that are over-represented compared to the general population.

A study for a UK bank, undertaking segmentation analysis to identify trends and patterns in their customers.

GitHub repo for customer data analysis to drive personalized marketing strategies and enhance engagement, loyalty, and revenue

Leveraging the Kaggle Online Retail Dataset (2009-2011), this system optimizes decision-making with: RFM Modeling for high-value customer identification, Ensemble Learning for purchase behavior prediction, Game Theory-Based Pricing for dynamic strategy optimization.

Identify Customer Segments

Customer segmentation for mail-orders in Germany, using unsupervised learning

Segmify is a comprehensive customer analysis tool that leverages RFM analysis, demographic insights, and buying behavior to segment customers and drive strategic business decisions. This project focuses on transforming raw customer data into actionable insights for improved marketing and sales strategies.

Django web application for comprehensive market analysis using machine learning and the Dunnhumby dataset

Customer Segmentation using the Recency, Frequency and Monetary Values

This repository contains code and analysis for performing RFM (Recency, Frequency, Monetary) analysis on retail store customer data. The analysis is followed by customer segmentation using the KMeans clustering algorithm to gain insights into customer behavior and enable data-driven marketing strategies.

Segment Sphere is a customer segmentation tool using RFM analysis to group customers based on recency, frequency, and monetary value. It processes e-commerce data, provides actionable insights, and visualizes results with interactive charts. Ideal for understanding customer behaviour and supporting data-driven decisions.

This project applies RFM analysis to segment customers based on purchasing behavior. It combines data cleaning, EDA, and RFM scoring to identify key customer groups and support targeted marketing, retention, and growth strategies.