Decision Tree Course
Gain expertise in decision trees, Random Forests, and gradient boosting for customer churn prediction. Learn to explain key drivers, apply practical feature engineering and evaluation techniques, and transform business intelligence data into stakeholder-ready, revenue-oriented actions and insights.
4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
This course teaches you to construct precise churn prediction models using raw customer data. You will cover data ingestion, cleaning, feature engineering, splitting methods, and evaluation metrics for imbalanced churn datasets. You will then implement decision trees, Random Forests, and Gradient Boosted Trees, interpret feature importance via SHAP, and convert model outputs into actionable retention strategies and experiments.
Elevify advantages
Develop skills
- Prepare churn-ready data: clean, profile, and engineer business intelligence features efficiently.
- Build and tune decision trees and ensemble methods: create, optimise, and evaluate impactful churn models.
- Handle imbalanced churn data: implement cross-validation, stratification, and class-weighting techniques.
- Achieve model explainability: apply SHAP, LIME, and feature importance for compelling business intelligence narratives.
- Derive actionable insights: convert churn drivers into focused retention strategies and testing plans.
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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