customer-segments

klaudia-nazarko/rfm-analysis-python

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

DionysiosZelios/RFM_analysis

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 promotions and also for future personalization services.

marcotav/unsupervised-learning

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.

SagarPatel98/Customer-Segmentation-using-Machine-Learning

This project is based on Unsupervised Learning

jjone36/Cohort

Customer Segmentation Anaylsis

fvanheer/RFM-Model

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.

JuzerShakir/Creating-Customer-Segments

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

mayur29/Udacity_Machine_Learning_Projects

These are all the assignments from Udacity Nanodegree Machine Learning course

nano-bot01/Customer-Segmentation-using-Clustering-

Customer Segmentation using Clustering (Machine Learning)

alimdsaif3/Customer_Segmentation_for_Retail_Shop

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.

cakmakaf/customer_segmentation

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

SKRohit/Customer_Segmentation

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

haataa/Identify_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.

moreirab/customer-segments

Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments

Venka97/Customer-segments

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

adityasahastrabudhe01/customer_segments

Udacity Machine Learning Engineer Nanodegree Unsupervised Learning Project: Creating Customer Segments

ashva7/customer_segments

Customer Segments - Machine Learning Nanodegree from Udacity

jetjodh/customer_segments

Project done in ML course

mmuppidi/customer_segments

Project: Creating Customer Segments using Unsupervised Learning

NoreenMekky/customer_segments

vishwasourab/customer_segmentation

In this project, I will analyze a dataset containing data on various customers' annual spending amounts (reported in monetary units) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.

ymubarak/Machine_Learning_Nanodegree

Projects of Udacity's Machine Learning Specialization Nanodegree program.

anshulika-prasad/udacity-ML-customer-segments

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

josancamon19/customer_segments

Creating Customer Segments for Udacity Machine Learning Engineer Nanodegree

Mazen-Embaby/customer_segments

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 mailout campaign. Your job as a data scientist will be to use unsupervised learning techniques to organize the general population into clusters, then use those clusters to see which of them comprise the main user base for the company. Prior to applying the machine learning methods, you will also need to assess and clean the data in order to convert the data into a usable form.

sanjeevai/customer_segments

Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.