Check whether the code follows the quality rules.

Clean Execution: Can the code run end-to-end without failing if the kernel is restarted?
Dependency Management: Are the libraries and exact versions documented (e.g., requirements.txt, Pipfile)?
Artifact & Data Versioning: Are data paths and model artifacts versioned (e.g., DVC, MLflow, immutable S3 URIs) rather than relying on local, hardcoded paths (C:/user/data.csv)?
Fixed Seeds: Is the random_state or seed fixed for all stochastic processes (data splits, weight initializations, ML algorithms) to guarantee deterministic results?

Credential Leakage: Are there any hardcoded passwords, API keys, access tokens, or DB URIs? Instruct the user to move these to environment variables or secret managers.
Sensitive Data (PII): Is the model exposing or directly logging Personally Identifiable Information (e.g., SSN, names, emails)? Ensure data is anonymized or hashed.
Query Sanitization: If the code dynamically queries databases, is it protected against SQL injection?

Memory Optimization: Are data types optimized (e.g., using float32 instead of float64, or category instead of object in Pandas)? Will the code break if data volume doubles?
Vectorization: Flag any iterative loops (like for loops or iterrows) over datasets. Suggest vectorized operations using NumPy/Pandas.
Framework Suitability: Does the data volume require distributed processing (e.g., Spark/Dask) instead of in-memory processing (e.g., Pandas/Scikit-learn)?

exp-review