Handle missing and unavailable predictive features

Scenario

You are developing a model to estimate whether a user will complete a successful tax filing (binary target success) for a product similar to TurboTax. One of the strongest predictors is:

• session_count = the total number of sessions the user has accumulated in the product. However:
• In training data, session_count contains many zeros and many missing entries.
• In production, stakeholders say session_count is unavailable at scoring time (when the prediction must be made), despite being present in the schema.
• Exploratory analysis indicates session_count is negatively correlated with success.

Questions

1. Data quality / missingness: How would you examine why session_count is frequently 0 or missing, and how would you handle those cases during modeling?
2. Training-serving skew: If session_count is unavailable at inference time, what choices do you have? How do you decide whether to (a) remove it, (b) build a proxy feature, or (c) adjust prediction timing or problem definition?
3. Interpretation: Give at least two plausible reasons for the negative relationship between session_count and success (including an “opposite viewpoint”), and explain what additional data or analyses you would use to confirm or reject each explanation.

Constraints / expectations

• Assume standard product event logs are available in principle (page views, step completions, timestamps), but instrumentation may be imperfect.
• Your answer should address leakage risk, feature availability, and how you would communicate tradeoffs to stakeholders.

Overview: This question tests a product analyst's machine learning ability to diagnose data quality problems, manage feature availability and training-serving skew, and interpret counterintuitive correlations, covering both conceptual understanding and practical application for model development and deployment.