Decision Tree Course
This course teaches you to master decision trees, random forests, and gradient boosting for accurate customer churn prediction. You will handle data preparation, model building, imbalanced datasets, explainability with SHAP and LIME, and transform insights into practical retention plans and business actions, all using real-world techniques for revenue impact.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Learn to create precise churn prediction models from customer data through data ingestion, cleaning, feature engineering, splitting methods, and metrics for imbalanced cases. Master decision trees, Random Forests, Gradient Boosted Trees, interpret results via SHAP, and develop actionable retention strategies and experiments.
Elevify advantages
Develop skills
- Prepare churn data: clean, profile, and engineer features quickly.
- Build decision trees and ensembles: construct, tune, and compare effective churn models.
- Handle imbalanced churn: use cross-validation, stratification, and class weights.
- Explain models: apply SHAP, LIME, and feature importance for business insights.
- Generate actions: convert churn factors into retention strategies and tests.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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