Dimensionality Reduction on Fish Dataset with Different Machine Learning Algorithms Comparison

Classification of fish is a complex task in the current situation exhibited by the great diversity of available species, even though it is used in agricultural and commercial sectors. In this work, the data is classified with the Supervised Machine Learning algorithms such as K-Nearest Neighbour, Naïve Bayes, and Decision Tree, observing the results before and after applying dimensionality reduction techniques PCA, LDA, and Truncate SVD. One of the well-known dimensionality reduction techniques Linear Discriminant Analysis LDA achieves the highest accuracy for K-NN, Naïve Bayes 93.7%, and Decision Tree 96.8%.