WINE QUALITY PREDICTION

This project predicts wine quality using various machine learning techniques. We used Logistic Regression, SVM, and Random Forests to classify wine samples based on their chemical properties. The project includes both static images and animated GIFs of the training process, highlighting the performance of each model in predicting wine quality.

Visualizations

Static Images

Pairplot:

Confusion Matrix - Decision Tree:

Confusion Matrix - Gradient Boosting:

Confusion Matrix - K-Nearest Neighbors:

Confusion Matrix - Logistic Regression:

Confusion Matrix - Random Forest:

Confusion Matrix - SVM:

Feature Importance - Gradient Boosting:

Feature Importance - Random Forest:

Model Accuracy Comparison:

Animations

Training Process - Logistic Regression:

Training Process - SVM:

Training Process - Random Forest:

Training Process - Decision Tree:

Training Process - Gradient Boosting:

Training Process - K-Nearest Neighbors: