Partial Label Learning based on GMM

Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree

In this project I have used unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories using a real-world dataset

PCA and Gaussian Mixture Model Clustering to uncover hidden segments in customer purchase data.

Unsupervised Learning ML project

In this project I use unsupervised learning techniques to identify different segments of costumers with different preferences for optimizing product delivery.

An elegant probability model for the joint distribution of wind speed and direction.

Unsupervised clustering of the UCI Wholesale Customer Spending Dataset

Comparision of human voice samples

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

An ML model which uses Gaussian Mixture Model clustering to classify customers.

Final project Of machine learning Nanodegree (Unsupervised Learning)

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

Customer Segments - Machine Learning Nanodegree from Udacity

Processing scripts and datasets for a project on medial vowels in Kaytetye

The project explores KMeans and Gaussian Mixture Algorithms to classify the Boston Housing Dataset into different groups based on different parameters.

Unsupervised Learning: Mixture of Balls With Different Volumes.

Use the credit card customer database to segement the groups by taking into account their spending patterns as well as past interactions with the bank

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