Knee point detection in Python 📈

Machine learning utility functions and classes.

Plotly-Dash NLP project. Document similarity measure using Latent Dirichlet Allocation, principal component analysis and finally follow with KMeans clustering. Project is completed with dynamic visual interaction.

Implementation of hierarchical clustering on small n-sample dataset with very high dimension. Together with the visualization results implemented in R and python

全球新冠肺炎的数据分析，包括基础知识有：kmeans算法设计，SSE算法设计，分级聚类算法设计，cophenetic distance 算法设计。

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.

EIGEN FREQUENCY CLUSTERING USING [KMEANS] [KMEANS & PCA ] [DBSCAN] [HDBSCAN]

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.

Enhancing the Performance of PSO Algorithm for Clustering High dimensional data using Autoencoders

This is pyspark based K-means clustering model which categorized Mobile Telecommunication customer based on their credit behaviors

Gender Recognition by Voice using KNN classification

API for grouping images on similarity.

Beer data clustering and pricing, evidence based pricing with Random Forest.

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

Finding optimal clusters for text data using tfids , silhoutte , elbow method , and kmeans

This repository contains multiple topics of Machine Learning.

K-means clustering, Evaluation methods of choosing k (Elbow Method, Silhouette analysis)

A simple web app to find the best color combinations from a picture

implements the elbow method to determine the optimal number of clusters (k) for a given dataset using the K-means clustering algorithm.

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.