Outlier multicollinearity free fish weight prediction using machine learning

Abstract

The water content and it purity completely depends on biotic species that are existing in the environment. The forecasted weight of the fish will be the fishing success and the challenge still prevails in the field of weight forecasting of fish. With this overview, this paper provide following contribution. Firstly, the Fish weight dataset from KAGGLE repository is subjected to da-ta pre-processing. Secondly, the raw data set is applied to find the regression relationship between all the features with target fish weight is done with visualization. Thirdly, the anova test is applied to verify features with PR(>F) < 0.05 that highly influence the Target. Fourth, the raw dataset and feature scaled dataset are applied to all the Linear and ensembling Regression models and performance are analysed. Fifth, feature correlation is examined and results shows that diagonal length, Vertical length and Cross length are having correlation of 1.0 which increases multicollinearity issue that leads to undesirable predictions. So those features are removed and fitted with Linear and ensembling Regression models and the performance are analysed. Sixth, the outlier predictions of the features are done and it removed by IQR Analysis and then fitted with Linear and ensembling Regression models and the performance are analysed with intercept, EVS, MAE, MSE and R2Score. Experimental Results shows that polynomial regression is able to achieve accuracy of 97% before and after feature scaling, outlier removal. Among ensembling, Gradient Boosting is providing the accuracy of 98% be-fore and after feature scaling, outlier removal.