Decision Tree Course
Gain expertise in decision trees, Random Forests, and gradient boosting to forecast customer churn accurately. Master feature engineering, handle imbalanced datasets, interpret models with SHAP and LIME, and transform predictions into practical retention plans and business insights for revenue growth.
flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
This course teaches building precise churn prediction models from customer data. Learn data preparation, feature engineering, model splitting, evaluation for imbalanced data, decision trees, Random Forests, Gradient Boosted Trees, SHAP interpretation, and converting results into retention strategies and experiments.
Elevify advantages
Develop skills
- Prepare churn data efficiently by cleaning, profiling, and engineering features.
- Build, tune, and evaluate decision trees and ensemble models for churn prediction.
- Handle imbalanced churn data using cross-validation, stratification, and class weights.
- Explain models clearly with SHAP, LIME, and feature importance for business reports.
- Convert model insights into targeted retention strategies and experiments.
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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