How to deal with bimodal feature in Logistic Regression? I am working on a binary classification problem where one of the most interesting features has a distribution which looks more or less bimodal. Here is the distribution plot of that feature:

The two modes seem to correspond to two classes. When I look at the distribution of this feature corresponding to each class separately, this is what I get:


Clearly, one of them is more like a log-normal distribution, and the other is more like normal, and the two peaks in the original distribution seems to correspond to two different classes. My question is how do I deal with this kind of bimodality in Logistic regression. Also, would other machine learning algorithms be more suitable for this kind of a problem?

Stick that feature in your regression like you would any other feature. Logistic regression makes no assumption that the features have a particular distribution. I suspect this misconception comes from the same confusion that people have about OLS linear regression.
Since the distribution of that feature for each $y$ category is not just a shift in location, you might benefit from using some nonlinear functions of this feature, such as a spline, but the distributions are so different that I expect this feature to give considerable discriminative ability on its own.
