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

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

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

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).

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

K-Prototype Clustering on Blood Transfusion Dataset

Customer segmentation for Ready, Steady Ride using K-Means (k=4) across weather, behavior and time perspectives - NOVA IMS ML project

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.

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

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

A Python project where I've grouped similar products in a real ecommerce dataset using some unsupervised learning techniques (clustering).

Built an unsupervised clustering model using K-Prototypes clustering and anomaly detection algorithms to discover patterns in the dataset containing 13000+ projects.

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