Project 5b Machine Learning Logistic Regression Tunning Hyperparameters with Sklearn

Hyperparameter tuning involves selecting the optimal values of hyperparameters, which affect the performance of the Logistic Regression model. Some common hyperparameters that can be tuned include solver, penalty , regularization strength,max_iter,warm_start,verbose,class_weight,multi_class,l1_ratio,n_jobs.The choices for Solver are "newton-cg", "lbfgs", "liblinear", "sag", "saga" the default option is "lbfgs".
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