This is an implementation of principle component analysis on the famous MNIST digit dataset. Firstly KNN has been trained on the data and is used for prediction obtaining an accuracy of more than 96%. Then the data is standardised using standard scaler class of scikit-learn. And then PCA is applied using scikit-learn and the cummulative variance percentage of all the principle components from 1 to 784 (since there are 784 features) has been plotted using matplotlib.