Abstract

Abstract: - Few computers have now proven particularly useful for implementing arithmetic and logic data. Their effectiveness in applications such as the face, objects, and voice recognition is not remarkable, especially in contrast to what the human brain can do. Machine learning algorithms were particularly useful for these kinds of applications in this study. They function similarly with the human brain by learning the data given and saving them for future data analysis identification. The emphasis has now been focused on increasing data storage and recovery, only neglecting the importance of the information given and the amount of data needed to be saved for data storage management. Therefore, this article explores the ability to minimize data storage by using separation and integrates it with a presented machine learning similarity detection algorithm. The Key Factor Analysis / Fisher Linear Discrimination Analysis (PCA) is used to minimize the vector dimension.Keywords: - Machine Learning, Data Storage Efficiency, Principle Component Analysis, Pattern Recognition etc