shaadclt / ElasticNet-MLflow-Regression

This repository contains code for training an ElasticNet regression model using MLflow. The model predicts the quality of wine based on various features.

MLflow ElasticNet Regression

This repository contains code for training an ElasticNet regression model using MLflow. The model predicts the quality of wine based on various features.

Requirements

• Python (>=3.6)
• MLflow (>=1.20.0)
• pandas
• numpy
• scikit-learn

Dataset

The dataset used for training the model is wine-quality.csv, which contains various features of wine samples along with their quality ratings.

Project Structure

• wine-quality.csv: Dataset file.
• train.py: Python script for training the ElasticNet regression model.
• README.md: This file.

Usage

1. Clone the repository:

git clone https://github.com/shaadclt/ElasticNet-MLflow-Regression.git

2. Navigate to the project directory:

cd ElasticNet-MLflow-Regression

3. Run the training script with desired alpha and l1_ratio values:

python train.py <alpha> <l1_ratio>

Replace 'alpha' and <l1_ratio> with the desired values for the ElasticNet hyperparameters. if not provided, default values (alpha=0.5, l1_ratio = 0.5) will be used.

Results

After running the training script, MLflow will log the model parameters, metrics and the trained model itself. You can view the results in the MLflow UI.

mlflow ui

License

This project is licensed under the MIT License.