In this repository I analyzed the white wine data set found at UCI machine learning repository. I used a variety of approaches such as a SVM with a rbf kernel, neural network and clustering algorithms to predict the quality of the wine based on the given meta-data:

• sulfur dioxide
• free sulfur dioxide
• residual sugar
• fixed acidity"
• volatile acidity
• alcohol
• sulphates
• pH
• density
• citric acid
• chlorides

The main libraries I used on this project are:

• Scipy
• Numpy
• Matplotlib
• Seabas
• Sklearn
• Pytorch