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.

A topic designed by Warwick Business School requires students to enhance loan portfolio management by utilising cluster analysis to group borrowers with similar characteristics, enabling personalised loan products, targeted marketing strategies, and a better customer support process to serve the unique needs of each segment through cluster analysis

In this project, I will be performing an unsupervised clustering of customer's records from a Retail Chain.

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.

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.

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

E-Commerce Customer Segmentation using k-means clustering

Customers RFM Clustering (Market Segmentation based on Behavioral Approach)

Identifying customer segments based on their purchasing behavior using RFM analysis and K-Means clustering.

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

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

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

Identifying customer segments for mail-orders company and performing a comparison with the general population

Identify Customer Segments

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

I worked on a data set to find the groups of people who have some kind of pattern. we used k-means clustering to find the pattern based on parameters available in the dataset.

Test repo for the Smartboard project

Customer Segmentation Using Elbow Method and K-Means Algorithm

Regression , Classification and Segmentation projects