I build a machine learning model to predict the quality of wine, employing various algorithms. Ultimately, I opted for the Elastic Net model, which yielded an accuracy of approximately 81%.

1. Update config.yaml
2. Update schema.yaml
3. Update params.yaml
4. Update the entity
5. Update the configuration manager in src config
6. Update the components
7. Update the pipeline
8. Update the main.py
9. Update the app.py

Clone the repository

https://github.com/entbappy/End-to-end-Machine-Learning-Project-with-MLflow

conda create -n wine python=3.8 -y

conda activate wine

pip install -r requirements.txt

# Finally run the following command
python app.py

Now,

open up you local host and port

Documentation

• mlflow ui

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/niloycste/Wine-Quality-Check-using-ML-Mlflow-DVC.mlflow
MLFLOW_TRACKING_USERNAME=niloycste
MLFLOW_TRACKING_PASSWORD=55343c50b515f85d83d1bbecdf5e45f6664231d3
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/niloycste/Wine-Quality-Check-using-ML-Mlflow-DVC.mlflow

export MLFLOW_TRACKING_USERNAME=niloycste

export MLFLOW_TRACKING_PASSWORD=55343c50b515f85d83d1bbecdf5e45f6664231d3

Click here to watch the video of this Ml Model Baesd App