I knew window functions were the move here but I second-guessed myself on whether to use a rolling average or just daily aggregates.
Start by clarifying the grain of the data and the modeling goal (e.g., user-level features for predicting purchase propensity). Then write SQL that aggregates daily activity into a single row per user (or user-variant) with meaningful features like total clicks, total purchases, conversion rate, and recency. Use conditional aggregation and window functions to handle time-based features and variant comparisons.
Pro tip: Always include a time window or recency feature (e.g., clicks in last 7 days) because raw totals can be dominated by long-term users and miss recent behavior shifts. Also, explicitly handle NULLs and divide-by-zero when computing rates.
Ask about the table schema (columns like date, user_id, variant, clicks, purchases) and the modeling objective (e.g., user-level prediction, variant comparison). Confirm the desired output grain (e.g., one row per user or per user-variant).
Decide whether to aggregate per user, per user-variant, or per variant. For modeling, user-level features are common, but if comparing variants, you might need variant-level aggregates or user-variant interactions.
Use SUM, AVG, COUNT, and conditional aggregation (CASE WHEN) to compute total clicks, total purchases, conversion rate (purchases/clicks), and activity days. Handle division by zero with NULLIF.
Use window functions or date filters to create recency features (e.g., clicks in last 7 days), trend features (e.g., week-over-week change), and ratios. Consider normalizing by tenure or active days.
Ensure the query returns one row per entity, handles NULLs, and includes all necessary features. Optionally, add a final SELECT to pivot variant-level features if needed for A/B testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with logistic since the target was purchases (binary).
Start by clarifying the problem context and data structure, then demonstrate a clean Python implementation using scikit-learn or statsmodels. Focus on interpreting coefficients in terms of the business metric, including direction, magnitude, and significance, while addressing common pitfalls like multicollinearity and scaling.
Pro tip: Use statsmodels for detailed coefficient statistics and scikit-learn for pipeline efficiency; always discuss the practical significance of coefficients in the context of Airbnb's marketplace (e.g., a 1-unit increase in X leads to Y% change in bookings).
Ask about the target variable (e.g., booking probability, price), feature types, and whether the goal is inference or prediction. Confirm if aggregated features are at listing, host, or city level.
Handle missing values, encode categorical variables, and scale features if needed. Use train-test split and fit a linear regression (for continuous target) or logistic regression (for binary target) with appropriate libraries.
Check R-squared, RMSE for linear; accuracy, AUC, confusion matrix for logistic. Discuss overfitting and regularization (L1/L2) if relevant.
For linear: a one-unit increase in feature X changes the target by β units, holding others constant. For logistic: a one-unit increase in X multiplies the odds by exp(β). Discuss statistical significance (p-values) and confidence intervals.
Mention multicollinearity (check VIF), interaction effects, and causality vs correlation. Suggest validating with A/B tests or using SHAP for feature importance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.