Customer Segmentation Analysis & Predict Consumer Behaviour
Christ Raharja
Build k-means customer segments, a decision-tree spending predictor and an SVM churn model, select features and interpret models for marketing decisions
Marketing focus
Apply k-means to RFM customer data, predict CLV tiers with a decision tree, build a churn pipeline and interpret commercial segments, alongside trade-area/store-location modeling
Haytham Omar-Ph.D · Udemy
Apply k-means to RFM customer data, predict CLV tiers with a decision tree, build a churn pipeline and interpret commercial segments, alongside trade-area/store-location modeling
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.
Christ Raharja
Build k-means customer segments, a decision-tree spending predictor and an SVM churn model, select features and interpret models for marketing decisions
DataCamp
Four chapters (16 videos, 53 exercises) implement marketing ML: logistic regression/decision trees and churn-driver interpretation on telecom data, RFM/linear-regression next-month CLV prediction for an online retailer, and k-means/NMF…
Vinay Karode
Implement logistic-regression and decision-tree churn models, interpret predictions and compare ensembles alongside broader insurance and retail-sales projects