Marketing focus
Python workflow covers ETL/Singer and Streamlit, causal/regression analysis, Prophet/ARIMA KPI forecasting, STL/S-H-ESD/Bayesian change-point anomaly detection, k-means/RFM, decision trees/random forests, LDA/QDA and PyMC Marketing BTYD…
Packt · Coursera
Python workflow covers ETL/Singer and Streamlit, causal/regression analysis, Prophet/ARIMA KPI forecasting, STL/S-H-ESD/Bayesian change-point anomaly detection, k-means/RFM, decision trees/random forests, LDA/QDA and PyMC Marketing BTYD customer lifetime value
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Related tasks, tools and formats. Check the differences before choosing.
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…
Packt
Four modules use marketing KPIs and performance measures including conversion, CPA and ROI, regression/decision-tree analysis covers conversion and churn, time series methods such as ARIMA and Prophet address seasonality and trends
DataCamp
Builds click-through-rate prediction from advertising data: feature creation, classification/decision trees, cross-validation, regularization, random forests and grid-search tuning, evaluates predictions against ad-spend ROI