Principal Component Analysis (PCA)

During the Data mining process, we are given raw data. Before visualizing or interpreting data, we have to make sure that certain refinement methods are applied to the data before it is available for analysis. This refinement process includes Preprocessing or cleaning the data, such as removing the null or blank values from the data. Next is the Feature selection or Feature Extraction Technique, which is utilized in PCA where the least contributing features are neglected or removed as per requirement. The last stage is the Data Transformation, where the user will apply normalization techniques to scale all the features in the same range.