I'm currently working on a data set where the goal is to predict the number of rented bikes in Seoul, given information about the weather at the time.
The data set can be downloaded here: https://archive.ics.uci.edu/dataset/560/seoul+bike+sharing+demand
One of the possible predictors is the variable rainfall, indicating the rainfall measured in millimetres at a given hour.
The distribution of that variable however is extremely skewed:
As you can see most of the observations had no rainfall at all, making the observations with rain almost invisible in the plot.
Also using other transformation techniques like the Box-Cox-Tranformation didn't yield any desirable results.
What would be an appropriate way to transform or use a variable like this as a regressor for a linear regression model?
Thank you very much for your help!
And the distribution of the log of the positive values: