Decision Tree Course
This course equips you with skills to build precise churn prediction models using decision trees, random forests, and gradient boosted trees. Learn data preparation, feature engineering, handling imbalanced datasets, model evaluation, and interpretation with SHAP values. Gain practical expertise to derive actionable retention insights from customer data, enabling revenue-focused business decisions and stakeholder presentations.
4 to 360 hours of flexible workload
certificate valid in your country
What Will I Learn?
Discover how to construct accurate customer churn prediction models from raw data. Master data ingestion, cleaning, feature engineering, splitting techniques, and metrics for imbalanced datasets. Implement decision trees, Random Forests, and Gradient Boosted Trees, interpret results using SHAP, and transform predictions into practical retention strategies and experiments.
Elevify Advantages
Develop Skills
- Prepare churn-ready data by cleaning, profiling, and engineering features efficiently.
- Build, tune, and compare decision trees and ensemble models for impactful churn prediction.
- Handle imbalanced churn data using cross-validation, stratification, and class weights.
- Explain models clearly with SHAP, LIME, and feature importance for business insights.
- Convert churn predictions into targeted retention strategies and experiments.
Suggested Summary
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