Principal component analysis technique is used to detect variation and find out specific patterns in a data record. It makes data visualization and exploration an easy process. This technique is used for identification and separation of a small number of principal components from a large dataset. It is effective in exploratory data analysis and predictive modeling. This statistical method is needed in fields like face recognition, computer graphics, neuroscience, image compression, social science, market research, and many others.
This easy to use and simple non-parametric analysis technique extracts the required information from a complex dataset quickly. It helps find out maximum variance with the smallest number of main components. Once the required data has been found, it becomes easier to compress the resulting data in the required format. It eliminates unrelated predictors, variables and multicollinearity. The process takes advantage of orthogonal transformation and is associated with canonical correlational analysis. It is sensitive to relative scaling of the original used variables. All industries that generate or use large datasets use it to make sense of those records.