Decision Tree Course
Gain expertise in decision trees, Random Forests, and gradient boosting for customer churn prediction. Learn practical feature engineering, evaluation techniques, model interpretation with SHAP, and how to transform insights into revenue-driving retention actions and experiments suitable for stakeholders.
4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
This course teaches you to create precise churn prediction models using customer data. You'll cover data ingestion, cleaning, feature engineering, splitting methods, and metrics for imbalanced datasets. Practice decision trees, Random Forests, Gradient Boosted Trees, interpret results with SHAP, and develop actionable retention strategies.
Elevify advantages
Develop skills
- Prepare churn data efficiently: clean, profile, and engineer features quickly.
- Build and tune decision trees and ensembles for effective churn models.
- Handle imbalanced data with CV, stratification, and class weights.
- Explain models using SHAP, LIME, and feature importance for business insights.
- Convert churn predictions into targeted retention plans 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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Giulio CarloDigital Marketing Student
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