_._._                       _._._
            _|   |_                     _|   |_
            | ... |_._._._._._._._._._._| ... |
            | ||| |  o NATIONAL BANK o  | ||| |
            | """ |  """    """    """  | """ |
       ())  |[-|-]| [-|-]  [-|-]  [-|-] |[-|-]|  ())
      (())) |     |---------------------|     | (()))
     (())())| """ |  """    """    """  | """ |(())())
     (()))()|[-|-]|  :::   .-"-.   :::  |[-|-]|(()))()
     ()))(()|     | |~|~|  |_|_|  |~|~| |     |()))(()
        ||  |_____|_|_|_|__|_|_|__|_|_|_|_____|  ||
    ~ ~^^ @@@@@@@@@@@@@@/=======\@@@@@@@@@@@@@@ ^^~ ~
        ^~^~                                ~^~^

A machine learning model that attempts to predict whether a loan from LendingClub will become high risk or not.

I predict that the RandomForestClassifier will be a better model due to the data set containing several columns of categorical data.

The logistic regression model seems to be performing better on the unscaled data. The random forest classifier is performing poorly, possibly due to overfitting.

I think the performance of the unscaled data is too poor in the random forest classifier to overcome the deficit of the logistical regression model. I predict both models will perform better after scaling.

It seems the prediction was incorrect due to random forest classifier actually performing worse after scaling. The logistic regression model performed significantly better after scaling.

The logistic regression model seems to be the way to go meaning my prediction was incorrect. The data set could possibly use some more pre-processing to achieve better results.