Run EDA and train models while preventing overfitting

Q: Run EDA and train models while preventing overfitting

This question evaluates proficiency in exploratory data analysis, feature preprocessing, baseline linear regression and small neural network training, as well as detecting and mitigating overfitting and selecting appropriate train/validation/test protocols and evaluation metrics for tabular regression.

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Question

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You are given a tabular regression dataset $\{(x^{(j)}, y^{(j)})\}_{j=1}^M$ with numeric and categorical features and a continuous target.

Describe (at a code-and-practice level) how you would:

Perform exploratory data analysis (EDA) to identify data issues and useful patterns.
Build a baseline linear regression model, including preprocessing steps.
Train a small neural network regressor.
Detect and mitigate overfitting for both models.

Be explicit about the train/validation/test protocol, metrics, and common pitfalls.

Run EDA and train models while preventing overfitting

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