rossmann-pharma-sales-prediction

A machine learning solution to forecast sales for Rossmann Pharmaceuticals' stores across various cities six weeks in advance. Factors like promotions, competition, holidays, seasonality, and locality are considered for accurate predictions. The project structure is organized to support reproducible and scalable data processing, modeling, and visualization.

Project Structure

├── .dvc/
│   └── config                        # Configuration files for data version control
├── .vscode/
│   └── settings.json                 # Configuration for VSCode environment
├── .github/
│   └── workflows/
│       ├── unittests.yml             # GitHub Actions workflow for running unit tests
api/
│
└── model/                              # Directory containing the saved model files
└── static/                              # Css files
├── templates/                           # templates rendering html
├── app.py                              # Main Flask application to call api
├── load_model.py                      # Load model functions
├── preprocessing.py                  # Preprocessing functions
├── .gitignore                        # Files and directories to be ignored by Git
├── requirements.txt                  # List of dependencies for the project
├── README.md                         # Project overview and instructions
├── scripts/
│   ├── __init__.py
│   ├── data_processing.py            # Script for data cleaning and processing
│   ├── data_visualization.py         # Scritpt for different plots
│   ├── load_data.py                  # Scritpt extracting and loading dataset
│   ├── hypothesis_testing.ipynb      # Script for hypothesis testing analysis
├── notebooks/
│   ├── __init__.py
│   ├── eda_notebook.ipynb            # Jupyter notebook for eda analysis
│   ├── hypothesis_testing.ipynb      # Jupyter notebook for hypothesis testing analysis
│   ├── data_preprocessing.ipynb      # Jupyter notebook for data preprocessing
│   ├── model_training.ipynb          # Jupyter notebook for statistical model training
│   ├── README.md                     # Description of notebooks
├── tests/
│   ├── __init__.py
│   ├── test_data_processing.py          # Unit tests for data processing module
│   
└── src/
    ├── __init__.py
    └── README.md                     # Description of scripts

Installation

git clone https://github.com/epythonlab/rossman-pharma-sales-prediction.git

cd rossman-pharma-sales-prediction

Create virtual environment

python3 -m venv venv # on MacOs or Linux

source venv/bin/activate # On Windows: venv\Scripts\activate

Install Dependencies

pip install -r requirements.txt

To run tests

navigate

cd tests/

pytest # all tests will be tested