Dimension reducing techniques are becoming more and more dominant in data science and model predictions because it is much more efficient and comfortable working on a small set of data than very large data. More often than not the reduced lower dimensional representation seems to contain the same properties as that of the higher dimensional space. Additionally, big sets of data prove to be a problem in terms of computational environment on both memory and processing power and hence the need for dimensionality reduction is key.