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Build a baseline classification model from messy data

CoinbaseMachine Learning Engineer · Onsite
MediumMachine Learning EngineerOnsiteMachine Learning

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This question evaluates skills in practical machine learning engineering, including data cleaning, preprocessing, feature selection, handling mixed numeric and categorical features, and baseline model construction for a binary classification task.

In a live notebook (e.g., Jupyter), you are given a messy, real-world tabular dataset for a binary classification problem.

Data characteristics

  • Target label: y ∈ {0,1}
  • Mix of numeric and categorical features
  • Missing values, inconsistent strings (e.g., "NA", empty), and possible outliers
  • Some columns may be identifiers (e.g., user_id, transaction_id) and should not be used as predictive features
  • Dataset is “medium-sized” (fits in memory); you can train a simple model quickly

Task Within the session, produce a working end-to-end baseline that:

  1. Loads the data and performs minimal but correct cleaning.
  2. Splits data into train/validation (and optionally test) without leakage.
  3. Builds a simple model that can handle mixed feature types (or uses preprocessing to enable this).
  4. Evaluates performance with an appropriate metric (e.g., ROC-AUC / PR-AUC / F1, depending on class imbalance).
  5. Briefly explains your choices (feature selection, preprocessing, model choice, and how you’d improve it if given more time).

You may choose only a few features if that helps you deliver a robust, working solution quickly.