🍷 Wine Quality Prediction - MLOps Pipeline

📋 Overview

This repository contains an end-to-end machine learning project with a comprehensive MLOps pipeline. The project demonstrates a complete workflow from data ingestion to model deployment with proper validation, transformation, training, and evaluation stages.

🚀 ML Pipeline Workflow

1. Data Ingestion - Download and extract data from source
2. Data Validation - Validate data schema and integrity
3. Data Transformation - Feature engineering and preprocessing
4. Model Training - Train and save machine learning model
5. Model Evaluation - Evaluate model performance and track with MLflow/DagsHub

📊 Project Structure

machinelearningproject/
│
├── .github/workflows/     # CI/CD workflows
├── artifacts/             # Generated artifacts during pipeline execution
├── config/                # Configuration files
│   └── config.yaml        # Main configuration
├── research/              # Jupyter notebooks for experimentation
│   ├── 1_data_ingestion.ipynb
│   ├── 2_data_validation.ipynb
│   ├── 3_data_transformation.ipynb
│   ├── 4_model_trainer.ipynb
│   └── 5_model_evaluation.ipynb
├── src/datascience/       # Source code
│   ├── components/        # Pipeline components
│   ├── config/            # Configuration management
│   ├── constants/         # Constants and paths
│   ├── entity/            # Data classes
│   ├── pipeline/          # Pipeline orchestration
│   └── utils/             # Utility functions
├── static/                # Static files for web interface
│   ├── css/               # Stylesheets
│   └── js/                # JavaScript files
├── templates/             # HTML templates
├── app.py                 # Web application
├── main.py                # Pipeline execution entry point
├── params.yaml            # Model parameters
├── schema.yaml            # Data schema definition
├── setup.py               # Package setup
└── Dockerfile             # Container definition

💻 Technologies Used

• Python - Primary programming language
• Jupyter Notebooks - Research and experimentation
• MLflow - Model tracking and versioning
• DagsHub - MLOps platform integration
• Web Interface - Interactive model interface with HTML/CSS/JavaScript

🛠️ Implementation Workflow

1. Update config.yaml with project configuration
2. Update schema.yaml with data schema definitions
3. Update params.yaml with model parameters
4. Update entity classes in src/datascience/entity/
5. Update configuration manager in src/datascience/config/
6. Implement components in src/datascience/components/
7. Create pipelines in src/datascience/pipeline/
8. Execute workflow through main.py

🌐 Web Application

The project includes a web interface for easy interaction with the trained model. The interface features:

• Input validation
• Sample data population
• Interactive prediction
• Responsive design

📈 MLflow Integration

The model is tracked using MLflow and integrated with DagsHub for experiment tracking and version control. Model metrics and artifacts are automatically uploaded to the DagsHub repository.

🚦 Getting Started

1. Clone the repository

git clone https://github.com/danialasim/machinelearningproject.git
cd machinelearningproject

2. Install dependencies

pip install -e .

3. Run the pipeline

python main.py

4. Launch the web application

python app.py

📄 License

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

🔗 Links

• MLflow Dashboard
• DagsHub Repository

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Created by Muhammad Danial - © 2025