I am predicting revenue using 8 predictors like units sold,units ordered, total order etc...using LM model(linear regression)
The range of revenue is 0 - 20K.
0 revenue's are those cases when customer purchased something online and then returned. I retain them as they are genuine purchase and 10% of records are having 0's.
The data is skewed so log transformation is done after replacing 0's with 1 as log can't be applied on 0. The predictors are not having outliers and they all in the range of 1 - 10. The highest correlation is 0.80 which is of units sold.
This is how the actual revenue (x axis) and prediction (y axis) below. Not that great!
Two major problem here
- For typical values prediction range is very high as seen in y - axis.
- 0 values are never predicted close to 0's.
RMSE of predicted values is 27 as compared to standard deviation of 128
On predicting 2nd time but without any transformation these is what i got below. Much better!
At least no extreme values in prediction. RMSE is as low as 4.
I would like to know what should be the approach here when predicting monetary values. What sought of transformation do i need to do or what other linear model do i need to try.
Please note i need to stick to linear model.
Edit: Model summary of log transformed data