I have a data with the following columns
col1 col2 col3 col4 label 7669.533073 7669.533073 7669.695497 7669.922593 1 7669.533043 7669.533072 7669.695487 7669.922596 0
the mean across all the 50 columns are similar and also the min and maximum.
I am trying to build a classifier and the best model(random forest) is giving me a recall of .55 (doesn't seem so good), could there be anything I am missing in this?
I have thought about normalising the data but there seems to be no need as all columns have a similar mean and std.
Is there any statistics technique I could apply to the data to help get an improved result.
Note the data is from a simulated crypto price and I am trying to predict the price movement (up or down)