{"blocks": [{"key": "b953ec10", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "da7a8da9", "text": "Feature engineering for a customer-propensity machine-learning model.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "66b0cd2d", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "a8313b66", "text": "When do we need to standardize or normalize variables? How would you handle numeric predictors that contain many null or zero values? If several features are highly correlated, how would you decide which one(s) to keep?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "17f4f19e", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "9c8c3a49", "text": "Discuss scaling impact, imputation vs flagging, and multicollinearity remedies.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}