This course teaches you how to calculate distance metrics, form and identify clusters in a dataset, implement k-means clustering from scratch and analyze clustering performance by calculating the silhouette score

This was done as a part of Springboard Data Science Career Track course (Unit10.5.4. Customer Segmentation Using Clustering).

It's the HAC algorithm that Im using to sort newspaper articles by news. You can adapt it to pretty much any type of text.

Capstone Project for the IBM Professional Certificate on Coursera

Credit Card Customer Segmentation

Clustream, Streamkm++ and metrics utilities C/C++ bindings for python

The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. How can the wholesale distributor use the customer segments to determine which customers, if any, would reach positively…

The objective of this project is to categorise the countries using some socio-economic and health factors that determine the overall development of the country and then accordingly suggest the NGO the country which is in dire need of help.

This has been done as a part of Data Science Career Track Course at Springboard.

Analyze past orders and create innovative features to build a customer's segmentation.

This project demonstrates a Clustering Model using Python. An international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. It has been able to raise around $ 10 million. The model is needed to help decide ho…

Clustering for Anuran Calls with 4 different families

This repository contains all program files and datasets used in implementation of Masters Thesis Research Work for the topic - "Efficient Clustering via Kernel Principal Component Analysis and Optimal One Dimensional Clustering".

Best Clustering using silhouette_score

Analysis of Optimal Pricing Strategies Maximizing the Profits of a Retailer Selling Jeans via Hierarchical Clustering

Grouping pupils according to the performance at two intermediate examinations

This repo holds a Clustering model written in PySpark.

Clustered customers into distinct groups based on similarity among demographical and geographical parameters. Applied PCA to dispose insignificant and multi correlated variances. Defined optimal number of clusters for K-Means algorithm. Used Euclidian distance as a measure between centroids.

This machine learning model makes, grouping a set of objects in such a way that objects in the same group(cluster) are more similar to each other than other groups(clusters).