Decision Tree Course
Gain expertise in decision trees, Random Forests, and gradient boosting for customer churn prediction. Master feature engineering, evaluation on imbalanced data, model interpretation with SHAP, and transforming insights into revenue-boosting retention plans and stakeholder presentations. Practical skills ensure clear, business-ready outcomes from raw data.
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 actionable retention strategies and experiments.
Elevify advantages
Develop skills
- Prepare churn data efficiently: clean, profile, and create business intelligence features quickly.
- Build and tune decision trees and ensembles like Random Forests for strong churn models.
- Handle imbalanced churn data using cross-validation, stratification, and class weights.
- Explain models clearly with SHAP, LIME, and feature importance for business intelligence narratives.
- Convert churn predictions into targeted retention strategies and testing initiatives.
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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