🍷 Decision Tree Classifier on Wine Quality Dataset

This project demonstrates the use of a Decision Tree Classifier to predict the quality of red wine. It applies a supervised machine learning approach to classify wine as Good or Bad based on various chemical properties.

---

📊 Dataset

• Source: Kaggle - Red Wine Quality Dataset
• Features: Fixed Acidity, Volatile Acidity, Citric Acid, Residual Sugar, Chlorides, Free Sulfur Dioxide, Total Sulfur Dioxide, Density, pH, Sulphates, Alcohol
• Target: Wine Quality (Binary Classification: Good (≥7), Bad (<7))

---

🧠 Model Used

• Algorithm: Decision Tree Classifier
• Criterion: Entropy
• Max Depth: 4
• Accuracy: ~88% (based on evaluation output)
• Evaluation Metrics: Accuracy Score, Classification Report, Decision Tree Visualization

---

⚙️ Tech Stack

• Python
• Pandas
• Scikit-learn
• Matplotlib

---

📸 Output

✅ Classification Report + Accuracy

✅ Visualized Decision Tree

---

👨‍💻 Author

Syed Imthiaz I
B.E. Computer Science and Engineering
KCG College of Technology

🔗 LinkedIn
🔗 LinkedIn Post

---

🚫 License & Disclaimer

© 2025 Syed Imthiaz I — All rights reserved.
Unauthorized copying, modification, distribution, or use of this code or any part of it is strictly prohibited without the express written permission of the author.

📩 For permission requests, contact: syedimthiaz2006@gmail.com

---