Address feedback to improve project

[celinehabashy]

Documentation

• Dependencies are listed within the methodology but should be more detailed (e.g., versions of Scikit-learn or Pandas).
• Missing examples of how to reproduce the analysis. Including a Jupyter notebook or script would help future users.
• Adequate documentation of the classifiers, but missing detailed preprocessing steps.

Code Quality

• Variable and function naming is descriptive, but splitting lengthy lines of code could improve readability.
• Code appears consistent with Python PEP8, though this is not explicitly stated.
• Good abstraction of the modeling process, but the exploratory data analysis could be modularized for reuse.
• missing explicit tests for preprocessing steps, model accuracy checks, or edge cases.

Reproducibility

• The data source is well-documented, but direct instructions to download or access the data are missing.

Analysis Report

• Background information is sufficient but could include more details about similar models or applications.
• No unit tests or validation steps
• The analysis mentions 47 misclassified instances but does not explore reasons behind these errors.