# Mixing exponential and linear regression with multiple predictors

This is the data set I am working on, trying to predict count (last column) :

    datetime,season,holiday,workingday,weather,temp,atemp,humidity,windspeed,count
2011-01-01 00:00:00,1,0,0,1,9.84,14.395,81,0,16
2011-01-01 01:00:00,1,0,0,1,9.02,13.635,80,0,40
2011-01-01 02:00:00,1,0,0,1,9.02,13.635,80,0,32
...


The count distribution is an exponential decrease. I tried basic linear regression, but the result is bad. So, I guess there is an exponential correlation between count and, at least, one of its predictors. I also guess there is linear correlation between count and other predictors as well.

How to mix multiple linear and exponential regression ? I am working using the anaconda distribution of python, but i'd also like to understand the theory of the model if possible. Thanks !

• The variable to count is the variable count (last column of my data). I expect some of the relation to be linear...in fact I don't have a clue how variables interact together. So I expect some of them to be linear. That's the point of regression, knowing how variables interact between each other, no ? – Moebius Apr 13 '16 at 15:29