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 properly, and deliver stakeholder-friendly insights that drive revenue through targeted retention actions.
from 4 to 360h flexible workload
certificate valid 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 values, and convert findings 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 for impactful churn predictions.
- Handle imbalanced churn data using cross-validation, stratification, and class weights.
- Explain models clearly with SHAP, LIME, and feature importance for business insights.
- Transform churn analysis into targeted retention strategies 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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