Classification of Handwritten Digits using Machine Learning Techniques

Abstract

ThenbspMNIST datasetnbsp(MixednbspNational Institute of Standards and Technologynbspdatabase) is a largenbspdatabasenbspof handwritten digits that is commonly used fornbsptrainingnbspvariousnbspimage processingnbspsystems. [1][2] The database is also widely used for training and testing in the field ofnbspmachine learning. The MNIST database contains 60,000 training images and 10,000 testing images. [3] In this paper, we aim to apply classification techniques to predict labels for records in the MNIST dataset using machine learning. In total, there are 10 labels ranging from 0-9. Classification will be done using Random Forest Classification Algorithm. We also aim to implement Principle Component Analysis to reduce the dimensionality of the data while retaining its variance. To this data, we aim to apply K Nearest Neighbors Classification Algorith