{"blocks": [{"key": "1630dc71", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "a1919482", "text": "Designing a churn-prediction model for a subscription product with messy real-world data.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "8fd22134", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "036e74d0", "text": "How would you handle missing values in the training data and justify your approach?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "30e163b9", "text": "Given this churn-prediction problem, which ML algorithm would you choose and why?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "33d0c461", "text": "Explain how Random Forest works, including voting, feature bagging, and depth control.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "b5d229d1", "text": "Define overfitting vs. underfitting and describe techniques to detect and mitigate each.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "7afe994a", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "b68bd7c3", "text": "Discuss imputation, ensemble strengths, cross-validation, regularisation, bias-variance trade-off.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}