# Data with similar mean, min and max across all columns. What could I do to build a classifier

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)

• Normalizing would be okay, but i would still do the subtraction first, before doing anything else with the data. Not sure how the common normalizing functions would handle this many significant digits, if there is any rounding there also. For data exploration, I usually like to look at histograms to start with. df.hist(bins=20, figsize=(20,15)) Jun 24 '20 at 15:39