An interactive approach to understanding Machine Learning using scikit-learn

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

The project involves performing clustering analysis (K-Means, Hierarchical clustering, visualization post PCA) to segregate stocks based on similar characteristics or with minimum correlation. Having a diversified portfolio tends to yield higher returns and face lower risk by tempering potential losses when the market is down.

Capstone Project for the IBM Professional Certificate on Coursera

Pytorch implementation of standard metrics for clustering

This case requires to develop a customer segmentation to understand customer's behaviour and separate them in different groups according to their preferences, and once the division is done, this information can be given to marketing team so they can plan the strategy accordingly.

This repository contains introductory notebooks for principal component analysis.

K-means is a least-squares optimization problem, so is PCA. k-means tries to find the least-squares partition of the data. PCA finds the least-squares cluster membership vector.

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.

Perform Clustering (Hierarchical, K Means Clustering and DBSCAN) for the airlines and crime data to obtain optimum number of clusters. Draw the inferences from the clusters obtained.

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.

A customer profiling project based on RFM (Recency, Frequency, Monetary) analysis using a dataset from an online retail company in the United Kingdom. The aim is to identify customer habits and create personalized marketing strategies for targeted advertising.

To perform customer segmentation using Python unsupervised learning model

Cryptocurrency classification system using dimensionality reduction with PCA & t-SNE and cluster analysis with K-Means

This repository contains introductory notebook for clustering techniques like k-means, hierarchical and DB SCAN

This project aims to assist stakeholders in selecting an optimal location for a new restaurant in Chennai, Tamil Nadu, India.

Best Clustering using silhouette_score

The purpose of this project is to create customer segmentations by using similarity between products purchased between the users by using Natural Language Processing techniques and Clustering

OptimalCluster is the Python implementation of various algorithms to find the optimal number of clusters. The algorithms include elbow, elbow-k_factor, silhouette, gap statistics, gap statistics with standard error, and gap statistics without log. Various types of visualizations are also supported.

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…