Segmentasi Pelanggan Menggunakan K-Means Clustering
Taman Belajar, Bina Nusantara University
Prepare customer data, implement k-means in Python, evaluate and interpret clusters and complete a customer-segmentation case study
Marketing focus
Learn hierarchical/k-means and categorical-data clustering, interpret dendrograms and apply the methods to market segmentation, a structured eight-chapter course
Institute of Product Leadership · Udemy
Learn hierarchical/k-means and categorical-data clustering, interpret dendrograms and apply the methods to market segmentation. A structured eight-chapter course
Selected published requirements: “No previous experience required - I'll teach you everything you need to know”. See the provider page for the full requirements and current tool terms
Practice: A quiz is listed in section eight. Its scoring is not exposed
Recorded Udemy course advertised with a public course preview. Exact lesson count may be unknown. Purchase or organization-subscription terms and current availability require checking
Related tasks, tools and formats. Check the differences before choosing.
Taman Belajar, Bina Nusantara University
Prepare customer data, implement k-means in Python, evaluate and interpret clusters and complete a customer-segmentation case study
O.P. Jindal Global University
Python marketing workflows cover k-means, hierarchical and DBSCAN segmentation, PCA, t-SNE and autoencoders, anomaly detection, association mining, semi-supervised methods, and recommender systems
Meta Brains , Skool of AI
Prepare and evaluate k-means, hierarchical and DBSCAN clusters, then complete an ecommerce customer-segmentation project, broader unsupervised ML supports the marketing application