ML-Powered Sales Forecasting with Dashboard

This project is a full-stack application that provides a B2B sales dashboard with predictive analytics. It consists of a React frontend, a Flask backend for ML model serving, and a servlet backend (Java) for additional server-side logic.

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Table of Contents

• Project Structure
• Features
• Getting Started
  • Backend Setup (Flask)
  • Frontend Setup (React)
  • Servlet Backend
• Usage
• Model Artifacts
• Scripts
• License

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Project Structure

ML Model Training.ipynb         # Jupyter notebook for model training
flask_backend/                 # Python Flask backend for ML inference
  server.py                    # Main Flask server
  util.py                      # Utility functions
  __pycache__/                 # Python bytecode cache
  .vscode/                     # VSCode settings
artifacts/                     # Model artifacts
  model.pickle                 # Trained ML model
  dictionary/                  # Feature dictionaries
  encoders/                    # Encoders for categorical features
react_frontend/                # React frontend
  public/                      # Static files
  src/                         # React source code
servlet_backend/               # Java servlet backend
h2h_milestone_3/Servers/       # Additional server resources

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Features

• Interactive Dashboard: Visualize B2B sales data.
• Predictive Analytics: Make predictions using a trained ML model.
• REST API: Flask backend serves predictions to the frontend.
• Modular Frontend: Built with React and modern best practices.
• Extensible Backend: Java servlet backend for additional business logic.

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Getting Started

Backend Setup (Flask)

1. Navigate to the backend directory:

   cd flask_backend

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Flask server:

   python server.py

   The server will start on http://localhost:5000 by default.

Frontend Setup (React)

1. Navigate to the frontend directory:

   cd react_frontend

2. Install dependencies:

   npm install

3. Start the React app:

   npm start

   The app will run on http://localhost:3000.

Servlet Backend

• The servlet backend is located in servlet_backend/.
• Setup and deployment instructions depend on your Java environment and servlet container (e.g., Tomcat).

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Usage

• Open http://localhost:3000 in your browser to access the dashboard.
• The frontend communicates with the Flask backend for predictions.
• Ensure both the Flask and React servers are running for full functionality.

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Model Artifacts

• Trained model and encoders are stored in artifacts/.
• The Flask backend loads these artifacts for inference.

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Scripts

React Frontend

• npm start — Runs the app in development mode.
• npm test — Launches the test runner.
• npm run build — Builds the app for production.
• npm run eject — Ejects the configuration (irreversible).

See react_frontend/README.md for more details.

Flask Backend

• python server.py — Starts the Flask server.

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License

This project is for educational purposes. Please see individual files for license information if applicable.

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Acknowledgements

• Create React App
• Flask
• scikit-learn, pandas, and other Python ML libraries

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For more details, refer to the documentation in each subdirectory.