Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

k-modes and k-prototypes clustering algorithms implementation in Go

Segmenting High profile doctors for Pharma company for maximising returns.

Classifying bank transactions with unsupervised k-prototypes clustering.

K-Prototypes with Differential Privacy

A machine learning project to predict Customers/Clients into correct segment to provide promotional information or for product advertising.

Asal Ikut Team (Clustering Mahasiswa Untuk Evaluasi Kinerja Perguruan Tinggi Menggunakan Algoritma KModes dan K-Prototypes)

An automated end-to-end data pipeline which performs customer clustering, stores output in data warehouse, and updates a live dashboard.

End-to-end machine learning pipeline designed for high-dimensional, mixed-type survey data. Implemented K-Prototypes Clustering, Factor Analysis of Mixed Data, and t-SNE manifold learning to successfully segment 1,400+ observations. Features rigorous feature engineering (ordinal encoding, MAR analysis) model validation via stability test (ARI=0.9).

k-prototypes for numerical and categorical clustering

Use case of K-prototypes algorithm for Customer Clustering.

Unsupervised Learning-based analysis on voter abstention data from Brazilian elections. Using clustering techniques, we aim to identify behavioral patterns in abstention reasons across demographic and regional variables.

Machine Learning Model For Customer Segmentation Using Algorithm K-Prototypes.

K-Prototype Clustering on Blood Transfusion Dataset

Cluster Analysis using K-Protopytes (on categorical variables) and Hierarchical Clustering uppon K-Medoids (on numeric variables) for Marketing Campaigns

Generating synthetic clusters to illustrate simple k-means and k-prototypes clustering

R-implementation of clustering using mixed-type data (continous and discrete)

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

Developing a proactive system for the telecom sector that predicts customer churn and segment at-risk users through predictive modeling and clustering.