Python Statistical Modeling Course
This course teaches Python-based statistical modelling for churn prediction. Participants will build interpretable logistic and tree models, conduct A/B tests, and convert data insights into actionable decisions for product and marketing strategies, delivering real business value in tech environments.
flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
Gain expertise in churn prediction using statistical models in Python. Learn to load and analyse data with pandas, engineer key features, perform exploratory analysis to identify churn factors, build logistic regression and tree-based models, evaluate them, and create reports, A/B tests, and ethical recommendations for business impact.
Elevify advantages
Develop skills
- Prepare churn datasets efficiently using pandas for loading, profiling, and cleaning.
- Create effective churn features like sessions, cohorts, recency, and engagement metrics.
- Develop and interpret logistic regression models with pipelines, AUC, and odds ratios.
- Evaluate and optimise tree-based models using SHAP and validation techniques.
- Convert statistical results into A/B tests, dashboards, and practical churn strategies.
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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