1. The wine data set athttps://archive.ics.uci.edu/ml/datasets/wine (Links to an externalsite.)Links to an external site. has 13 features. Develop in Pythonand apply your own version of the PCA algorithm to this data set,to visualize how PCA helps with dimensionality reduction. Explainhow many Principal Components you will choose and why. What percentof the variance in the data do the selected Principal Componentscover?
For the implementation, you may use any objects, modules, andfunctions in NumPy, SciPy and other python libraries to do variousoperations such as to compute the eigen values, vectors or performany other math / linear algebra operation, but not use the PCAfunction
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