PCA is a statistical procedure that uses an orthogonal transformation which converts a set of correlated variables to a set of uncorrelated variables. PCA is a most widely used tool in exploratory data analysis and in machine learning for predictive models. Moreover, PCA is an unsupervised statistical technique used to examine the interrelations among a set of variables. The PCA model consist of ‘2’ number of components(n_components) followed by a Logistic Regression model with parameters – solver = ‘lbfgs’ , multi_class = ‘auto’.
The datasets used in this study are available at - https://data.matr.io/1