Build a predictive model from TurboTax sample data

Q: Build a predictive model from TurboTax sample data

This question evaluates predictive modeling skills including target selection, label leakage prevention, feature engineering from product and historical interaction data, model selection and evaluation, and considerations for deployment and interpretability using a tax-preparation sample dataset.

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Question

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You receive a TurboTax sample dataset (user-level and/or session-level) and are asked to build a predictive model.

Task

Pick a concrete prediction target (choose one and justify):
- Probability a user will file within 14 days of starting.
- Probability a user will churn (not file this season).
- Expected revenue from the user this season.
Describe how you would:
- Define labels and avoid label leakage.
- Build features from product interactions and historical attributes.
- Choose a baseline model and at least one stronger model.
- Evaluate performance (metrics, calibration, slice performance).
- Turn the model into an actionable recommendation (e.g., targeting, prioritization, interventions).

Constraints / realism

Data may be missing or delayed.
Class imbalance is likely (e.g., churn).
The business cares about interpretability and safe deployment.

Build a predictive model from TurboTax sample data

Task

Constraints / realism

Solution

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Build a predictive model from TurboTax sample data

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Task

Constraints / realism

Solution

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