Build A Purchase-Intent Prediction Notebook
Build A Purchase-Intent Prediction Notebook
Build a machine learning notebook that predicts whether a user session will lead to a purchase. The training and test CSV files contain page counts, time spent, exit and dropoff rates, engagement score, promotion indicator, encoded user device and traffic fields, holiday flag, and the target outcome.
Deliverables:
- A documented notebook.
submissions.csvwithidand binaryoutcomepredictions.
Evaluation Metric: Weighted F1.
Constraints & Assumptions
- The target is binary.
- The test set has no target labels.
- Some fields may contain bugs or unwanted values.
- Columns with more than 50 percent missing values or constant values should be dropped.
- The notebook may use one or more models.
Clarifying Questions to Ask
- What counts as an unwanted value for each field?
- Is class imbalance severe?
- Should categorical integer fields be treated as categories or ordinal numbers?
- Is model interpretability required?
- What validation split should match the hidden evaluation?
AI interview prep
Core dilemma
Balancing model complexity and generalization for a binary classification task with potentially noisy and imbalanced data, while meeting a specific evaluation metric (weighted F1).
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Build A Purchase-Intent Prediction Notebook
Build A Purchase-Intent Prediction Notebook
Build a machine learning notebook that predicts whether a user session will lead to a purchase. The training and test CSV files contain page counts, time spent, exit and dropoff rates, engagement score, promotion indicator, encoded user device and traffic fields, holiday flag, and the target outcome.
Deliverables:
- A documented notebook.
submissions.csvwithidand binaryoutcomepredictions.
Evaluation Metric: Weighted F1.
Constraints & Assumptions
- The target is binary.
- The test set has no target labels.
- Some fields may contain bugs or unwanted values.
- Columns with more than 50 percent missing values or constant values should be dropped.
- The notebook may use one or more models.
Clarifying Questions to Ask
- What counts as an unwanted value for each field?
- Is class imbalance severe?
- Should categorical integer fields be treated as categories or ordinal numbers?
- Is model interpretability required?
- What validation split should match the hidden evaluation?