Principal Component Analysis For Dimensionality Reduction For Animal Classification Based On LR

Nowadays, data generation is huge in nature and there is a need for analysis, visualization and prediction. Data scientists find many difficulties in processing the data at once due to its massive nature, unstructured or raw. Thus, feature extraction plays a vital role in many applications of machine learning algorithms. The process of decreasing the dimensions of the feature space by considering the prime features is defined as the dimensionality reduction. It is understood that with the dimensionality reduction techniques, redundancy could be removed and the computation time is decreased. This work gives a detailed comparison of the existing dimension reduction techniques and in addition, the importance of Principal Component Analysis is also investigated by implementing on the animal classification. In the present work, as the first phase the important features are extracted and then the logistic regression (LR) is implemented to classify the animals.