Decision Tree Course
Gain expertise in decision trees, Random Forests, and gradient boosting for customer churn prediction. Learn feature engineering, handling imbalanced data, model evaluation, and explaining predictions with SHAP to deliver actionable insights that drive revenue through targeted retention efforts and clear business intelligence.
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. Practice building decision trees, Random Forests, and Gradient Boosted Trees, interpret results with SHAP, and convert findings into practical retention strategies and tests.
Elevify advantages
Develop skills
- Prepare churn data efficiently by cleaning, profiling, and engineering key features.
- Build, tune, and evaluate decision trees and ensemble models for strong churn predictions.
- Handle imbalanced churn data using cross-validation, stratification, and class weights.
- Explain models clearly with SHAP, LIME, and feature importance for business reports.
- Transform churn insights into specific retention plans and experiments.
Suggested summary
Before starting, you can change the chapters and 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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