A clustering tutorial with scikit-learn for beginners.

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

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

Jupyter Notebook: documentation of the implementation of the ToMATo algorithm to the GUDHI library (Topological Data Analysis), using real datasets.

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

Dynamic Time Warping model with hierarchical clustering

code for Visualizing and Understanding the Relationship between PCA, Auto encoder and K-Means Clustering.

Github Repo for CSE 573 project : Document Clustering and 3D Visualization

The projects displayed are part of my course in Great Learning - Business Analytics and Business Intelligence

Python implementation of K-Means Clustering algorithm for unsupervised learning. Efficiently groups data points into clusters based on similarity. Simple yet powerful tool for data exploration, segmentation, and pattern recognition tasks in various fields.

Apply unsupervised learning techniques to identify customers segments.

Extracting the dominant colours from an image using k-means clustering.

GUI version of https://github.com/guglielmosanchini/ClustViz

Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments

Time series clustering studies

Unsupervised Learning

I leverage the Spotify Million Song Dataset, which includes song titles, artist names, song links, and lyrics. I compare popular clustering techniques and implement a simple recommendation system ♫⋆｡♪ ₊˚♬ ﾟ.

Clustering Starter Kit

Unsupervised machine learning: creating segments of customers

It's a website that recommends books from database to users based on ratings given by other users. Two recommender models are built viz. 1) Popularity Based Recommender 2) Using Collaborative Filtering Algorithm