💹 K-Means clustering implementation in TypeScript

Develop a customer segmentation to define marketing strategy. Used PCA to reduce dimensions of the dataset and KMeans++ clustering technique is used for clustering and profiling of clusters.

This is an end-to-end project that focuses on predicting credit card default using machine learning techniques. The project includes data validation,data preprocessing, model training, evaluation, and deployment.

Implementation of the FLS++ algorithm for K-Means clustering.

Green Space Design Company Team Assignment

k-means clustering in TypeScript

K-Means++ Clustering using Gap Statistic for determining optimal value of K in Python

Neighbor Search and Clustering for Time-Series using Locality-sensitive hashing and Randomized Projection to Hypercube. Time series comparison is performed using Discrete Frechet or Continuous Frechet metric.

KMeans and KMeans++ in Spark

🃏 Determine the MTG metagame using K-means++ clustering

Stanford Scalable K-Means++ implementation in C++ with benchmarking.

A clustering (object categorization) algorithm, with an implementation of K-means and K-means++

A small, header-only, parallel implementation of kmeans clustering for arbitrary-long byte vectors.

Jupyter notebook with Object Oriented implementation of the k-means clustering algorithm. Experimenting with both random and k-means initialization.

Explore my solo Customer Segmentation Project, diving into data analysis, clustering, and visualization. Uncover distinct customer segments for tailored marketing strategies and enhanced engagement. Discover the power of data-driven insights in this independent project.

Go library implementing Kmeans++ and Elkan's Kmeans algorithm

Typescript로 구현해 보는 KMeans

K-Means Algorithm implemented using sequential and parallel algorithms.