Notebook Interview Example: Ad Click Prediction

In this final part of the section, we bring all the core concepts together through a realistic, end-to-end ML interview example. The walkthrough demonstrates how to apply best practices across the entire notebook round—from EDA to model evaluation—with clarity, intention, and real-world relevance.

• In EDA, we go beyond surface checks by exploring unique value counts, visualizing target distribution, spotting class imbalance, and identifying trends like CTR by device type—ensuring every insight informs downstream modeling.
• In feature engineering, we thoughtfully create new features (e.g., age buckets, time-of-day flags), handle multicollinearity, and tailor transformations based on domain intuition, not just standard recipes.
• During model training, we justify our choice of random forest for its robustness and interpretability, apply cross-validation and stratified splits, and run grid search for tuning—demonstrating intention behind every step.
• In evaluation, we use metrics that align with the problem (precision, recall, F1, AUC-ROC, confusion matrix) and interpret results in context, showing we understand the real-world stakes of false positives and negatives.
• For follow-up questions, we proactively discuss improvements (e.g., deeper models, richer features), production monitoring (CTR by segment, drift detection), and handling class imbalance—proving we think like end-to-end ML practitioners, not just coders.

This example illustrates how to move from “checking boxes” to demonstrating thoughtful, production-aware problem-solving under real interview conditions.

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