Build k-means customer segments, a decision-tree spending predictor and an SVM churn model, select features and interpret models for marketing decisions
Build k-means customer segments, a decision-tree spending predictor and an SVM churn model. Select features and interpret models for marketing decisions
Audience and starting point
Selected published requirements: “No previous experience in customer analytics is required”. “Basic knowledge in python and statistics”. See the provider page for the full requirements and current tool terms
Access and subscriptions
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
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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
12 sections • 164 lectures • 15h 42m total lengthCheck current priceEnglish
Four chapters (17 videos, 55 exercises) use online-retailer transactions for cohort acquisition/retention metrics, RFM value scoring and custom segments, then prepare and scale RFM features, fit k-means and interpret the resulting…
Intermediate Python, four hours. 17 videos and 55 interactive exercises. Prerequisite: Supervised Learning with scikit-learnCheck current priceAsk about teaching language