Decision Tree Course
Master decision trees, random forests, an' gradient boosting fi predict customer churn, explain di drivers, an' turn BI data inna clear, revenue-focused actions wid practical feature engineering, evaluation, an' stakeholder-ready insights. Yuh will build accurate models from scratch, handle imbalanced data like a pro, an' create actionable retention strategies dat drive real business results.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
This course teaches yuh how fi build solid churn prediction models from raw customer data. Yuh will learn data ingestion, cleaning, feature engineering, splitting methods, an' evaluation metrics fi imbalanced churn. Den yuh practice decision trees, Random Forests, an' Gradient Boosted Trees, interpret feature importance wid SHAP, an' turn model results inna clear retention insights an' experiments.
Elevify advantages
Develop skills
- Churn-ready data prep: clean, profile, an' engineer BI features quick-quick.
- Decision Trees & ensembles: build, tune, an' compare high-impact churn models.
- Imbalanced churn handling: apply CV, stratification, an' class-weight tactics.
- Model explainability: use SHAP, LIME, an' feature importance fi clear BI stories.
- Actionable insights: turn churn drivers inna targeted retention an' test plans.
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 are saying
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