Marketing focus
Python/scikit-learn supervised workflows include marketing-engagement prediction, product recommendation, three-month customer lifetime value, neural-network churn prediction and model deployment
O.P. Jindal Global University · Coursera
Python/scikit-learn supervised workflows include marketing-engagement prediction, product recommendation, three-month customer lifetime value, neural-network churn prediction and model deployment
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Related tasks, tools and formats. Check the differences before choosing.
DataCamp
Dedicated Telco Churn workflow: exploratory analysis, feature selection/engineering, supervised scikit-learn prediction, train/test split, confusion matrix and accuracy/precision/recall/ROC-AUC/F1, tuning and feature importance
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…
Christ Raharja
Analyze campaign and retention metrics, use unsupervised customer segmentation, build CatBoost churn and MLP lifetime-value models, and run A/B tests within a broader Excel/Python/Power BI course