Implement logistic-regression and decision-tree churn models, interpret predictions and compare ensembles alongside broader insurance and retail-sales projects
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Implement customer segmentation, social sentiment, ecommerce recommendations, customer lifetime value and association rules within a broader 21-project Python ML portfolio. These outputs differ from the same creator’s retained…
1 section • 21 lectures • 6h 41m total lengthCheck current priceEnglish
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 (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…
Intermediate Python, four hours. 16 videos and 53 interactive exercises. Prerequisite: Supervised Learning with scikit-learnCheck current priceAsk about teaching language