Detailed explanation for this can be found here: https://analyticsdefined.com/introduction-random-forests/

In this markdown, I've used German credit card dataset and used SMOTE to handle class imbalance and then I've used Logistic and Random Forest to predict if the probability of fraud. The datasets contain transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. Dataset link: https://www.kaggle.com/dalpozz/creditcardfraud