Logistic regression

Up to this point, we have talked about logistic regression as a model: formula, sigmoid, probability, decision boundary. All of this is important, but by itself remains theory. Cases are needed to show how this model works on real tasks. There will be no "perfect" examples here. The data may be simple or noisy, the features obvious or strange, the decisions not always clear-cut. That is normal. This is exactly how logistic regression is used in practice.

• Case 1. Logistic regression for customer churn
• 🔒 Case 2. Newsletter subscription
• 🔒 Case 3. Spam or not spam
• 🔒 Case 4. Ad click (CTR)
• 🔒 Case 5. Loan approval
• 🔒 Case 6. Fraud vs normal transaction
• 🔒 Case 7. Medical screening
• 🔒 Case 8. Equipment technical failure