This project performs an in-depth analysis and prediction of wine quality using machine learning techniques. It explores various data preprocessing, visualization, and classification approaches to determine factors that impact wine quality.
- Source: UCI Machine Learning Repository
- Features: 11 physicochemical attributes (like acidity, pH, alcohol, etc.)
- Target: Quality score (0-10)
- Null value detection
- Distribution plots for all features
- Correlation heatmap
- Feature importance analysis
- Logistic Regression
- Decision Tree Classifier
- Random Forest Classifier
- Support Vector Machine (SVM)
- Gradient Boosting
Each model was evaluated using accuracy, confusion matrix, and classification report.
Performance metrics like:
- Accuracy
- Precision, Recall, F1-score
- Confusion matrix
- ROC Curve (if implemented)
- Feature(s) like alcohol, volatile acidity, and sulphates showed strong correlation with wine quality.
- The best-performing model (e.g., Random Forest) achieved an accuracy of ~X% (fill this based on your results).