Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data
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Updated
Jun 19, 2024 - Python
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.
Use case of K-prototypes algorithm for Customer Clustering.
k-prototypes for numerical and categorical clustering
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).
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
Medical data clustering: K-Prototypes, elbow method, silhouette analysis, pairwise statistical tests, automated Word reporting. pandas, scikit-learn, kmodes.
Generating synthetic clusters to illustrate simple k-means and k-prototypes clustering
R-implementation of clustering using mixed-type data (continous and discrete)
Customer segmentation for Ready, Steady Ride using K-Means (k=4) across weather, behavior and time perspectives - NOVA IMS ML project
Pipeline de segmentacion de clientes con K-Prototypes + DBSCAN. Dashboard Streamlit interactivo. 355k registros | 3 clusters | RF acc 99.65%
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