Decision Tree Course
Learn decision trees, random forests, and gradient boosting to forecast customer churn, understand key drivers, and transform business intelligence data into practical, revenue-boosting actions through hands-on feature engineering, model evaluation, and stakeholder-friendly insights.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
This course teaches building precise churn prediction models from customer data. Cover data ingestion, cleaning, feature engineering, splitting methods, and metrics for imbalanced datasets. Practice decision trees, Random Forests, Gradient Boosted Trees, interpret with SHAP, and develop actionable retention strategies.
Elevify advantages
Develop skills
- Prepare churn data: clean, profile, and engineer features quickly.
- Build decision trees and ensembles: create, 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 clear insights.
- Create actionable plans: convert churn drivers into retention strategies and tests.
Suggested summary
Before starting, you can change the chapters and the 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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