Marketing focus
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
DataCamp · 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
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Related tasks, tools and formats. Check the differences before choosing.
University of Colorado System
Marketing applications of R-based CART and random forests, cross-validation/model comparison, causal trees and forests for targeting, segmentation, principal-component analysis and recommender systems
Packt
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…
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