{"blocks": [{"key": "96efce9c", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "019512bf", "text": "Predicting next-period ad conversion rate using historical campaign data (adid, date, impressions, clicks, conversions).", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "e045b657", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "3ac09478", "text": "How would you predict conversion_rate for the upcoming period?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2c8452c3", "text": "Do we need to transform the target conversion_rate and why?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "ff60bb15", "text": "If more data were available, what additional features would you add?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "c24ed736", "text": "How would you evaluate model performance?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2a514afe", "text": "Explain logistic regression’s loss function.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "4873fb66", "text": "Describe PCA’s eigenvalues, eigenvectors, and its assumptions.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "18342727", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "0da6dea2", "text": "Consider logistic/beta regression with time-series lags; evaluate with log-loss, AUC, calibration; PCA assumes linearity & orthogonality of components.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}