data/: Contains subdirectories for raw data (as obtained), processed data (cleaned and preprocessed), and external data (not generated by the project).
notebooks/: Jupyter notebooks for exploratory data analysis, visualizations, and experimentation. Keep them organized and consider using them to explain your workflow.
src/: Holds the main source code.
utils/: Utility functions used across the project.
models/: Contains machine learning models or statistical models if applicable.
data_processing/: Scripts or modules for data cleaning, preprocessing, and transformation.
visualization/: Code for data visualization, including plots, graphs, etc.
tests/: For unit tests to ensure the code functions as expected. It can also include test data.
docs/: Documentation related to the project. This could include explanation files, how-to guides, or any relevant documentation.
requirements.txt: A file containing the list of Python dependencies required for the project. You can generate it by using pip freeze > requirements.txt.
README.md: Essential information about the project, including setup instructions, how to use the project, and any additional details required to understand and run the code.
