MLflow-Wine-Quality-Project

I've built a model to predict wine quality and set up a remote server on DagsHub for tracking experiments.

• ElasticnetWineModel

• Experiments

Video Presentation

MLflow-WQ-Presentation.mp4

DATASET

This dataset is taken from : http://archive.ics.uci.edu/ml/datasets/Wine+Quality

Acknowledgements

Thanks to MLflow for providing this tutorials and examples

Installation

1. Clone the repository:

   git clone https://github.com/CodeWithCharan/MLflow-Wine-Quality-Project.git
   

2. Create a virtual environment (optional): Virtual Environment Set Up

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Run app.py:

   python app.py
   

5. Go to mlflow ui:

   mlflow ui
   

6. mlflow ui will be running on http://127.0.0.1:5000/, so paste this URL

7. Now, try different experiments and compare them:

   python app.py 0.3 0.7
   

8. Create a remote server (optional): You have the option to integrate the project with any remote server, such as AWS, Azure, GCP, etc. In this project, I have used Dagshub as a remote server : https://dagshub.com/user/login