This file describes the improvements made to the "Wine Quality Prediction" project based on reviewer feedback. Each improvement is documented with references to specific evidence in the repository, including relevant code changes, commit messages, and pull requests.
This document highlights the improvements made to the project based on the feedback provided by reviewers. It includes references to specific changes, supported by URLs pointing to the evidence in the codebase, commits, or pull requests (PRs).
Feedback: Functions in the script lacked detailed documentation regarding their purpose, inputs, and outputs.
Action: Added detailed docstrings to all functions in the script.
- Evidence: Commit includes comprehensive function-level documentation.
Feedback: The references section did not mention the computational tools and libraries used (e.g., Altair, NumPy).
Action: Added references to all computational tools and libraries used in the project.
- Evidence: See PR #70, which updates the references section in the report.
Feedback: The environment setup in docker had a missing dependency for the deepchecks package.
Action: Updated the Dockerfile file to include the deepchecks package.
- Evidence: Commit shows the updated environment file.
Feedback: Tables (e.g., classification report) were created manually instead of being generated programmatically.
Action: Updated the scripts to generate all tables programmatically.
- Evidence: PR #72 demonstrates the implementation of dynamic table generation for reproducibility.
Feedback: Figures in the report had mismatched titles and numbers (e.g., "Figure 3" labeled as "Figure 1").
Action: Corrected the figure titles and numbering in the report.
- Evidence: PR #73 includes the fixed titles and updated numbering.
Feedback: Figures 2 and 4 included both Python code and resulting images, leading to redundancy.
Action: Removed redundancy by including either the code or the images, not both.
- Evidence: PR #71 simplifies the figures section.
Feedback: Tables and figures should be dynamically generated using code for reproducibility.
Action: Ensured all tables and figures in the report are generated dynamically.
- Evidence: PR #74 provides updated scripts for dynamic generation.
Feedback: The target variable needed more explanation, including what the classes represent.
Action: Added detailed descriptions of the target variable and the meaning of the quality classes.
- Evidence: Commit updates the dataset description in the report.
Feedback: Dataset features were not well-described, making it difficult for readers unfamiliar with wine to understand their importance.
Action: Added a detailed description of each dataset feature.
- Evidence: PR #85 provides the updated feature descriptions.
Feedback: The contributing.md file lacked details on how to make a contribution.
Action: Updated the file to include detailed contribution instructions.
- Evidence: PR #79 contains the revised
contributing.md.
Feedback: A dedicated directory for analysis.ipynb was missing.
Action: Created a new directory for analysis files.
- Evidence: Commit.
Feedback: The report did not specify a Creative Commons license.
Action: Added a Creative Commons license to the project.
- Evidence: PR #81 introduces the license.
Feedback: The mamba package version was not specified in the environment.yml file.
Action: Updated the environment.yml file to pin the mamba version.
- Evidence: Commit.
Feedback: The latest tag was used for Docker images instead of a specific version.
Action: Updated the Docker configuration to use specific image tags.
- Evidence: PR #76.
These improvements address all feedback provided by reviewers and ensure the project is more robust, reproducible, and user-friendly.