Data analysis using principal component analysis

In this paper, we have evaluated an algorithm using Principal Component Analysis (PCA) for its application in data analysis. In the research field, it is very difficult to understand the large amount of data and is very time consuming too. Therefore, in order to avoid wastage of time and for the ease in understanding we have scrutinized a PCA algorithm that can reduce the huge dimension of the data into 2-dimensional. The method of PCA is used to compress the maximum amount of information into first two columns of the transformed matrix known as the principal components by neglecting the other vectors that carries the negligible information or redundant data. The main objective of the paper is to separate two compounds say A and B having different concentrations for all four sensors and identifies which sensors have the similar or different concentration with the help of various plots that explains the correlation between the different variables.