Dimensionality Reduction in Machine Learning Technique using Principal Componenet Analysis

Abstract

Machine learning plays a vital role in today’s world. In the internet data searching will be abundant. These data has to be trained to the system in order to perform the work of machine learning.  In machine learning the main parameter that is the size of the data plays a vital role in the execution time. In other words we can say that the execution time is directly proportional to the volume of data. The data that are going to use for our research is been collected from a private production organization. When the analysis of the organization is done the datasets that is been collected in enormous in used. To simply the work and to get accurate production results dimensional reduction method is followed. For data reduction the Principal Component Analysis (PCA) technique is used. This technique can be used to reduce the size of the data that we will execute when finding out the production of an organization. The dimensionality reduction helps in finding out the exact volume of data that is been present in the datasets and reduce its size and helps in giving the exact accuracy results. In this paper we have analyze the concept of Principal Component Analysis along with clustering algorithm.