A curated list of awesome customer analytics content

This repository contains RFM analysis applied to identify customer segments for global retail company and to understand how those groups differ from each other.

This project is based on Unsupervised Learning

RFM (Recency, Frequency, Monetary) analysis is a proven marketing model for behavior based customer segmentation. It groups customers based on their transaction history – how recently, how often and how much did they buy. RFM helps divide customers into various categories or clusters to identify customers who are more likely to respond to promot…

This repo contains unsupervised models including the Latent Dirichlet Allocation (LDA) model applied to a corpus of research papers and a clustering analysis applied to customer segmentation.

Customer Segmentation Anaylsis

Basic RFM model to kickstart customer value segmentation. This project aims to guide the first time user to have a bouncing board into setting up their first segmentation model.

Applied Unsupervised Learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.

These are all the assignments from Udacity Nanodegree Machine Learning course

Customer Segmentation using Clustering (Machine Learning)

Code to perform clustering using self organizing maps on retail customer data.

Customer segmentation is a process where we divide the consumer base of the company into subgroups. We need to generate the subgroups by using some specific characteristics so that the company sells more products with less marketing expenditure.

We apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data

Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments

This project identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data has been provided by Bertelsmann Arvato Analytics.

This project uses unsupervised learning techniques and decomposition methods to find meaningful structure in the data.

In this project, you will work with real-life data provided to us by our Bertelsmann partners AZ Direct and Arvato Finance Solution. The data here concerns a company that performs mail-order sales in Germany. Their main question of interest is to identify facets of the population that are most likely to be purchasers of their products for a mail…

Project done in ML course

Projects of Udacity's Machine Learning Specialization Nanodegree program.

My final submitted project for udacity basic ML course, customer segments