π·π About the Model:
- Predicts the quality of red wine based on its chemical composition.
- Built using Random Forest Classifier for accurate results.
- Features:
- 11 input features, including alcohol, acidity, sugar content, etc.
- Target: Quality score from 3 to 9.
- Split data into training and testing sets for evaluation.
π Performance:
- Achieved an impressive accuracy score of 72.5% on the test set.
- Visualized decision tree structure for better understanding.
π How to Use:
- Clone the GitHub repository:
git clone https://github.com/username/red-wine-quality-prediction.git
- Install required libraries (listed in
requirements.txt
). - Run the Jupyter Notebook (
Wine-quality Model.ipynb
) to train and evaluate the model.
π Note:
- This model is suitable for predicting the quality of red wine. For other types of wine, a different model may be needed.
- Results may vary based on the specific dataset and hyperparameter tuning.
π Learning Resources:
π€ Contributions Welcome:
- Feel free to contribute to this project by submitting pull requests.
- Share your feedback and suggestions to improve the model further.
π· Enjoy Predicting Red Wine Quality! π·πο»Ώ