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.

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.

This repository contains code for my Machine Learning Basic Nanodegree Project.

Udacity Machine Learning Engineer Nanodegree Capstone on customer segmentation and acquisition with Arvato Bertelsmann Financial Solutions.

Test repo for the Smartboard project

Identify Customer Segments

This repository contains project materials for the Spring 2024 STAT 208 class, specifically for Team 8. All materials are the property of Team 8, University of California, Riverside, A. Gary Anderson School of Management. Thank you for viewing our repository.

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

This project analyzes e-commerce order fulfillment using Advanced SQL Techniques and Python-based visualization to uncover insights on sales trends, customer segmentation, shipping cost optimization, and payment preferences.

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 using unsupervised ML

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

Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments

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.