Comparing Logistic regression, Support vector machine, Decision tree, Random forest and Gradient Boosting models for binary classification ML problem.
This project is about modeling a binary classification ML algorithm. This project uses 80/20 train test split with KFolds equals to 5 for cross validation. First step is the data processing. Secondly, Fitting the data into Logistic regression, Support vector machine, Decision tree, Random forest and Gradient Boosting. Storing the F1-scores for train data and solver of each model. Finally, choosing the best model based on best test score.