Dimensionality-Reduction-using-PCA-LDA-and-t-SNE

• Report consists of inferences gathered after implementing dimensionality reduction techniques like Principal Component analysis(PCA) , Linear Discriminant Analysis(LDA) , t-sNE(t-distributed stochastic neighbor estimation) and a maximal margin classifier (Support Vector Machine - SVM) on datasets like Labelled Faces In Wild(LFW) and the infamous Fischer Iris data.
• Python notebook has the corresponding code .