Wine Quality Prediction

An end-to-end machine learning project for predicting wine quality using physicochemical properties. This project implements a complete ML pipeline with data ingestion, validation, transformation, model training, and evaluation using MLflow for experiment tracking.

🍷 Project Overview

This project predicts the quality of red wine based on its physicochemical properties such as acidity, sugar content, and alcohol level. The system uses a structured ML pipeline architecture with configuration management and experiment tracking.

Key Features

• Automated ML Pipeline: Complete end-to-end workflow from data ingestion to prediction
• Configuration Management: YAML-based configuration for easy parameter tuning
• Experiment Tracking: MLflow integration for model versioning and metrics tracking
• Web Interface: Flask-based web application for real-time predictions
• Data Validation: Schema-based data quality checks
• Modular Architecture: Clean separation of components for maintainability

🚀 ML Pipeline Components

1. Data Ingestion

• Downloads wine quality dataset from specified source
• Extracts and stores data in structured format

2. Data Validation

• Validates data against predefined schema
• Generates status reports for data quality checks

3. Data Transformation

• Feature engineering and preprocessing
• Train-test split preparation
• Data scaling and normalization

4. Model Training

• Trains machine learning model on processed data
• Hyperparameter optimization support
• Model serialization with joblib

5. Model Evaluation

• Comprehensive model performance evaluation
• MLflow integration for experiment tracking
• Metrics logging and model registry

🛠️ Installation

Prerequisites

• Python 3.8+
• Git

Setup Instructions

1. Clone the repository

   git clone <repository-url>
   cd Wine-Quality-Prediction

2. Create and activate virtual environment

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Unix/MacOS
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

📊 Dataset

The project uses the Wine Quality dataset containing physicochemical properties:

• Fixed acidity
• Volatile acidity
• Citric acid
• Residual sugar
• Chlorides
• Free sulfur dioxide
• Total sulfur dioxide
• Density
• pH
• Sulphates
• Alcohol
• Quality (target variable)

🏃‍♂️ Usage

Training the Model

Run the complete ML pipeline:

python main.py

Web Application

Start the Flask web application:

python app.py

Access the application at http://localhost:8080

• Home Page: Input wine properties for prediction
• Train Endpoint: /train - Triggers model training
• Predict Endpoint: /predict - Returns quality predictions

📁 Project Structure

Wine-Quality-Prediction/
├── config/
│   └── config.yaml          # Pipeline configuration
├── src/
│   └── WineQualityPrediction/
│       ├── components/      # ML pipeline components
│       ├── pipeline/        # Pipeline orchestration
│       └── utils/           # Utility functions
├── templates/               # Flask HTML templates
├── research/               # Notebooks and experiments
├── artifacts/              # Generated artifacts (models, data)
├── app.py                  # Flask web application
├── main.py                 # Main training script
├── schema.yaml             # Data schema definition
├── params.yaml             # Model parameters
└── requirements.txt        # Python dependencies

⚙️ Configuration

Key Configuration Files

• config.yaml: Pipeline paths and data sources
• schema.yaml: Data validation schema
• params.yaml: Model hyperparameters

Updating Configuration

To modify the pipeline:

1. Update config.yaml for data paths and sources
2. Update schema.yaml for data validation rules
3. Update params.yaml for model parameters
4. Update entity classes in src/WineQualityPrediction/entity/
5. Update configuration manager in src/WineQualityPrediction/config/
6. Update components in src/WineQualityPrediction/components/
7. Update pipeline in src/WineQualityPrediction/pipeline/
8. Update main.py if needed

📈 Model Performance

The model is evaluated using:

• Accuracy metrics
• Confusion matrix
• Classification report
• Cross-validation scores

All metrics are logged to MLflow for tracking and comparison.

🎓 Skills & Knowledge Gained

Machine Learning & Data Science

• End-to-End ML Pipeline Development: Building complete machine learning workflows from data ingestion to deployment
• Data Preprocessing & Feature Engineering: Handling missing values, scaling, and feature transformation
• Model Training & Evaluation: Implementing classification algorithms and performance metrics
• Experiment Tracking: Using MLflow for model versioning, parameter tracking, and reproducibility

Software Engineering & Architecture

• Modular Code Design: Creating maintainable, reusable components with clear separation of concerns
• Configuration Management: Implementing YAML-based configuration systems for flexible parameter management
• Data Validation: Schema-based data quality checks and validation pipelines
• Design Patterns: Applying software design patterns for scalable ML systems

Web Development & Deployment

• Flask Web Applications: Building RESTful APIs and web interfaces for ML models
• Model Deployment: Serving machine learning models through web endpoints
• Template Integration: Using Jinja2 templates for dynamic web content
• API Development: Creating robust prediction endpoints with error handling

DevOps & MLOps

• Version Control: Git workflow management with branching strategies
• Dependency Management: Virtual environments and requirements.txt management
• Model Serialization: Using joblib for efficient model persistence
• Containerization: Understanding Docker concepts for application deployment

Technical Tools & Libraries

• Data Manipulation: Advanced pandas operations and numpy array handling
• Visualization: Creating insightful plots with matplotlib
• File Handling: Working with various file formats (CSV, YAML, JSON)
• Error Handling: Implementing robust exception handling and logging

Domain Knowledge

• Wine Chemistry: Understanding physicochemical properties that affect wine quality
• Feature Importance: Identifying key factors that influence wine quality ratings
• Data Analysis: Exploratory data analysis and feature correlation analysis

🔧 Technologies Used

• Machine Learning: scikit-learn
• Data Processing: pandas, numpy
• Experiment Tracking: MLflow
• Web Framework: Flask
• Configuration: PyYAML, python-box
• Visualization: matplotlib
• Model Serialization: joblib

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

📄 License

This project is licensed under the terms specified in the LICENSE file.

📞 Contact

For questions or suggestions, please open an issue in the repository.