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)

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

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.

K-Prototype Clustering on Blood Transfusion Dataset

k-prototypes for numerical and categorical clustering

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

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

Developed a Customer Segmentation for a fictional insurance company by using several clustering algorithms. Project done for Data Mining course of Data Science Ms

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).

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

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

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