The aim of the project is to implement atleast 2 clustering methods to partition the wine dataset and atleast 2 classification methods to distinguish between different classes. These methods are then compared and evaluated using various evaluation metrics and conclusions are drawn from the results obtained.

The dataset used for this project is "Wine Recognition dataset", which is a toy dataset provided by Sci-kit learn library.The dataset contains 13 predictive attributes and 3 classes which each of the record can be classified to. The data is the results of a chemical analysis of wines grown in the same region in Italy by three different cultivators. There are thirteen different measurements taken for different constituents found in the three types of wine.