Prinicpal component analysis and fish species classification Fish classification It is the process of identifying and classifying fish families and species based on their physical characteristics. The project uses concepts of data exploration, principal component analysis and classification using supervised algorithms. The classification algorithms studied are Linear Discriminant Analysis, Logistic Regression(LR), Gradient Booster Classifier, K-NEAREST NEIGHBOR Dataset information (raw)- This dataset provides information of 7 common different fish species that are Bream, Roach,Whitefish, Parkki, Perch, Pike and Smelt. The dataset consists of 6 features for classifying the species of fishes. The features used to describe the fishes are Weight (weight of fish in Gram g), Length1(vertical length in cm), Length2(diagonal length in cm), Length 3(cross length in cm), Height (height in cm) and Width (width in cm). It includes 159 entries for these attributes and finally it contains a column titled ‘Species’ which is the label that identifies the type of the fish.