End-to-End Inventory Optimization & Data Science Platform

A Flask-based web application for inventory management and optimization, featuring various tools for demand forecasting, safety stock calculation, and inventory analysis.

Features

• Data Management

  • File upload and processing
  • Data cleaning and preprocessing
  • Data type conversion
  • Data visualization

• Inventory Analysis

  • Safety stock simulation
  • Economic Order Quantity (EOQ) calculation
  • Inventory turnover analysis
  • Cost of inventory analysis

• Demand Forecasting

  • Time series analysis
  • Seasonal decomposition
  • Multiple forecasting methods
  • Visualization of forecasts

• Machine Learning

  • Classification and regression models
  • Feature engineering
  • Model training and evaluation
  • Prediction capabilities

Installation

1. Clone the repository:

git clone https://github.com/JosephAni/datasage.git
cd datasage

1. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  

Note:

• Always activate the virtual environment with source venv/bin/activate before running the app or installing packages.

• Install all requirements inside the venv:

  pip install -r requirements.txt

• If you use an IDE, set the Python interpreter to venv/bin/python for this project.

---

Optional: Use a run.sh Script for Convenience

You can create a run.sh script to automatically activate the venv and run the app:

#!/bin/bash
source venv/bin/activate
python app.py

Make it executable:

chmod +x run.sh

Then run your app with:

./run.sh

To run docker docker build -t datasage

1. Create a .env file with the following variables:

SECRET_KEY=your-secret-key-here
DATABASE_URL=sqlite:///inventory.db
DEBUG=True

1. Run the application:

python app.py

The application will be available at http://localhost:8080

Project Structure

inventory-optimization/
├── app.py                 # Main application file
├── requirements.txt       # Python dependencies
├── .env                  # Environment variables
├── .gitignore            # Git ignore file
├── static/               # Static files (CSS, JS, images)
│   ├── css/
│   └── js/
└── templates/            # HTML templates

Dependencies

• Flask 3.0.2
• Flask-SQLAlchemy 3.1.1
• pandas 2.2.1
• numpy 1.26.4
• scikit-learn 1.4.0
• matplotlib 3.8.2
• plotly 5.18.0
• And other dependencies listed in requirements.txt

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

Joseph Ani - GitHub

Project Link: https://github.com/JosephAni/datasage

To activate platform run nix-shell dev.nix

Running with Docker

To build and run the app using Docker (with the fixed base image):

docker build -t datasage .
docker run -p 8080:8080 datasage

The application will be available at http://localhost:8080]