Add wine quality prediction

[SayantikaLaskar]

Issue #219

This project welcomes participants of all skill levels, guiding them through data preprocessing, model building, and evaluation using real-world datasets.

Key Objectives:

• Explore Wine Quality Metrics: Understand attributes like acidity and sugar content.
• Data Preprocessing: Clean and prepare data for analysis.
• Feature Engineering: Create new features to capture complex relationships.
• Model Selection: Experiment with algorithms like Linear Regression, Decision Trees, Random Forests, SVM, and Neural Networks.
• Model Evaluation: Use metrics like MAE and R² Score to assess performance.
• Hyperparameter Tuning: Optimize models for better predictions.
• Visualization: Employ tools like Matplotlib and Seaborn for insightful data visualizations.

[github-actions]

Thank you for submitting your pull request! 🙌 We'll review it as soon as possible. In the meantime, please ensure that your changes align with our CONTRIBUTING.md. If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊