@lerabyte: Inside the Mind of a Data Scientist 🧠 I didn’t start by picking a CatBoost model right away - I started by understanding the customer data, the class imbalance, and what the business actually needs from the model. Because more customers stay than leave, accuracy alone could be misleading. The real goal is to catch as many likely churners as possible while keeping false alarms manageable, which is why I focused on PR-AUC, precision, recall, and the prediction threshold. CatBoost was a strong fit because the dataset mixes numerical features like tenure and monthly charges with categorical features like contract type, internet service, and payment method. Instead of making a simple yes-or-no decision, the model produces a churn probability that can be used to prioritize which customers may need support first. Full code: github.com/lerabyte/customer-churn-catboost #ml #machinelearning #datascience #ai #STEM

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Saturday 11 July 2026 22:13:52 GMT