Project Structure

This section describes the organization of the Store Sales - Deep Learning Solution project. The structure follows best practices for reproducibility, modularity, and scalability, inspired by the Cookiecutter Data Science template.

Directory Layout

├── LICENSE
├── Makefile
├── [README.md](http://_vscodecontentref_/0)
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- Documentation and project reports.
├── models             <- Trained and serialized models, model predictions, or model summaries.
├── notebooks          <- Jupyter notebooks for exploration and analysis.
├── [pyproject.toml](http://_vscodecontentref_/1)     <- Project configuration and metadata.
├── references         <- Data dictionaries, manuals, and explanatory materials.
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures for reporting.
├── [requirements.txt](http://_vscodecontentref_/2)   <- Python dependencies for the project.
├── [setup.cfg](http://_vscodecontentref_/3)          <- Configuration for code style tools.
└── store_sales_DL     <- Source code for this project.
    ├── __init__.py
    ├── config.py               <- Project configuration variables.
    ├── dataset.py              <- Data loading and preprocessing scripts.
    ├── features.py             <- Feature engineering code.
    ├── modeling
    │   ├── __init__.py
    │   ├── predict.py          <- Model inference code.
    │   └── train.py            <- Model training code.
    └── plots.py                <- Visualization code.

Key Components

• data/: Contains all data files, organized by processing stage.
• docs/: Project documentation and MkDocs files.
• models/: Saved models and prediction outputs.
• notebooks/: Jupyter notebooks for exploration and analysis.
• store_sales_DL/: Main source code for data processing, feature engineering, modeling, and visualization.
• Makefile: Automation commands for running the pipeline.
• requirements.txt: List of Python dependencies.

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This structure ensures clarity, maintainability, and ease of collaboration for both development and production workflows.