Decision Tree Course
Gain expertise in decision trees, Random Forests, and gradient boosting for accurate customer churn prediction. Learn essential data preparation, model tuning for imbalanced data, explainability techniques like SHAP and LIME, and how to derive actionable, revenue-boosting retention plans from business intelligence data with stakeholder-friendly insights.
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 raw customer data. You'll cover data ingestion, cleaning, feature engineering, data splitting methods, and metrics for imbalanced datasets. Hands-on practice includes decision trees, Random Forests, Gradient Boosted Trees, interpreting results with SHAP values, and converting predictions into practical retention strategies and experiments.
Elevify advantages
Develop skills
- Prepare churn analysis data: clean, profile, and create business intelligence features efficiently.
- Build and optimise decision trees and ensemble models like Random Forests for impactful churn predictions.
- Handle imbalanced churn data using cross-validation, stratification, and class weighting techniques.
- Explain models clearly with SHAP, LIME, and feature importance for compelling business intelligence narratives.
- Transform churn model outputs into targeted retention strategies and experimental plans.
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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