Decision Tree Course
Gain expertise in decision trees, Random Forests, and gradient boosting for customer churn prediction. Master feature engineering, handling imbalanced data, model evaluation, explainability with SHAP and LIME, and transforming insights into revenue-boosting retention plans and experiments suitable for business stakeholders.
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. Learners cover data ingestion, cleaning, feature engineering, splitting methods, and metrics for imbalanced datasets. It includes hands-on work with decision trees, Random Forests, Gradient Boosted Trees, SHAP for feature importance, and converting results into practical retention strategies and tests.
Elevify advantages
Develop skills
- Prepare churn data efficiently: clean, profile, and create business intelligence features quickly.
- Build and optimise decision trees and ensemble models like Random Forests for impactful churn predictions.
- Manage imbalanced churn data using cross-validation, stratification, and class weighting techniques.
- Explain models clearly with SHAP, LIME, and feature importance for compelling business narratives.
- Convert churn analysis into targeted retention strategies and experimental 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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