Build a baseline classification model from messy data

Q: Build a baseline classification model from messy data

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.

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Question

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:

Loads the data and performs minimal but correct cleaning.
Splits data into train/validation (and optionally test) without leakage.
Builds a simple model that can handle mixed feature types (or uses preprocessing to enable this).
Evaluates performance with an appropriate metric (e.g., ROC-AUC / PR-AUC / F1, depending on class imbalance).
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.

Build a baseline classification model from messy data

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Solution

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