Clusteval provides methods for unsupervised cluster validation

The Project focuses on Customers and Company, you have to analyze the sentiments of the reviews given by the customer in the data and made some useful conclusion in the form of Visualizations. Also, cluster the zomato restaurants into different segments. The data is visualized as it becomes easy to analyse data at instant.

I have clustered similar movies and TV Shows available on Netflix taking into account of attributes like Description, Cast, Director, Genre etc of a particular movie/show.

Customers RFM Clustering (Market Segmentation based on Behavioral Approach)

Customer Segmentation using R

This repository will be for our Geolog Silhouette Cluster Analysis

Customers RFM Clustering (Market Segmentation based on Behavioral Approach)

A cluster analysis leveraging the kmeans algorithm to determine which degrees are likely to yield which levels of income based on historical data.

Comparing the Elbow Method and Silhouette Method for choosing the optimal number of clusters in K-Means algorithm

This Repository uses K-Means Clustering Algorithms , Silhouette Analysis and Elbow method in order to understand the customers like who can be easily converge [Target Customers] so that the sense can be given to marketing team and plan the strategy accordingly

For an UK based non-store online retail for which we need to cluster it's customers in to different groups so that we can run targeted campaign for each group

Segmenting with Mixed Type Data - A Case Study Using K-Medoids on Subscription Data

Customer Behavior Analysis

Using Spotify data to create a recommendation system for The Beatles

Implementing K-Means clustering for research about environmental awareness and environmental practices of Ecuadorian households regarding the enviroment

An investment advisory firm needs to segment stock offerings so they may offer their customers understandable investment options.

2019.07.08 2등 KCB 금융스타일 시각화 경진대회(KCB/DACON)

Perform Principal component analysis and perform clustering using first 3 principal component scores both Heirarchial and K Means Clustering and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data.