Decision Tree Course
Gain expertise in decision trees, Random Forests, and gradient boosting to forecast customer churn, uncover key drivers, and transform business intelligence data into straightforward, revenue-boosting actions through hands-on feature engineering, model evaluation, and insights ready for stakeholders.
flexible workload from 4 to 360h
valid certificate in your country
What will I learn?
This course teaches you to create precise churn prediction models using customer data. You will cover data ingestion, cleaning, feature engineering, data splitting, 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 experiments.
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 effective churn prediction.
- Handle imbalanced churn data using cross-validation, stratification, and class weights.
- Explain models clearly with SHAP, LIME, and feature importance for business storytelling.
- Convert churn analysis into targeted 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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