Data Preprocessing Using Enhanced Principal Component Analysis (Epca) For Agricultural Datasets

Abstract

Data pre-processing is considered as the core stage in core and data mining. Standardization, discretization, and dimensionality decrease are notable strategies in data pre-processing. In this paper proposed Enhanced Principal component Analysis (EPCA) the effects of pre-processing strategies on the Agricultural for the accuracy of the dataset. Experiments were conducted utilizing the above-listed techniques and their singular outcomes were contrasted with one another. Enhanced PCA were tried for dimensionality decrease; besides, an existing methodology of PCA and KNN was attempted and the presentation showed superior characterization accuracy contrasted with the individual strategies.