Decision Tree Course
Gain expertise in decision trees, random forests, and gradient boosting to forecast customer churn, uncover key drivers, and transform business intelligence data into practical, revenue-boosting actions through hands-on feature engineering, robust evaluation methods, and stakeholder-friendly insights that drive real results.

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
This course teaches building precise churn prediction models from customer data. Learn data ingestion, cleaning, feature engineering, splitting techniques, and metrics for imbalanced datasets. Master decision trees, Random Forests, Gradient Boosted Trees, interpret results using 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: tune and compare effective churn models.
- Handle imbalanced data: use cross-validation, stratification, and class weights.
- Explain models: apply SHAP, LIME, and feature importance for insights.
- Create actionable plans: convert churn drivers into retention strategies.
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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