{"blocks": [{"key": "a13dd61b", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "0bfd33cd", "text": "A subscription platform wants to predict whether a customer will churn in the next month.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "15ee2c35", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2a4d9d60", "text": "Outline the end-to-end workflow—from feature engineering through model deployment—to build a churn predictor. 2. Which evaluation metrics would you prioritize and why? 3. How would you handle severe class imbalance during training?", "type": "unordered-list-item", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "861ddd6d", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "f9348302", "text": "Talk about train/validation split, cross-validation, ROC-AUC, precision-recall, SMOTE/weighted loss, monitoring.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}