Decision Tree Course
Gain expertise in decision trees, Random Forests, and gradient boosting for customer churn prediction. Learn to explain key drivers, engineer features effectively, evaluate models, and transform business intelligence data into actionable, revenue-boosting retention plans and stakeholder insights.
from 4 to 360h 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 loading, cleaning, feature creation, data splitting methods, and metrics for imbalanced datasets. You'll apply decision trees, Random Forests, Gradient Boosted Trees, interpret results via SHAP, and convert findings into practical retention strategies and tests.
Elevify advantages
Develop skills
- Prepare churn data efficiently: clean, profile, and build business intelligence features quickly.
- Master decision trees and ensembles: construct, optimise, and evaluate powerful churn prediction models.
- Handle imbalanced churn data: use cross-validation, stratification, and class weights effectively.
- Achieve model transparency: apply SHAP, LIME, and feature importance for compelling business narratives.
- Generate practical outcomes: convert churn factors into focused retention initiatives and experiments.
Suggested summary
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