This project applies the Gaussian Naive Bayes (GaussianNB) classifier to the Wine dataset for classifying wines into three categories based on their chemical properties. The dataset consists of 13 features, such as alcohol content, color intensity, and flavonoids, used to predict the wine class.
Data Preprocessing: The dataset is loaded and prepared for training.
Model: A Gaussian Naive Bayes classifier is trained on the dataset.
Evaluation: Model performance is assessed using accuracy and confusion matrix.
Python 3.x
The model successfully classifies wine into three categories with a good accuracy rate.