# Intuitive visualization of data

I am implementing a recommendation engine tool used by several users who are supplying feedback in the form of thumbs up (+1) or thumbs down (-1) for each recommendation. The recommendation engine uses 3 different models independently to suggest recommendations (i.e each model suggests its own unique recommendations). The feedback data is in a pandas dataframe df shown below.

df.head()
recommendation_id    feedback    model      x1      x2
0978hb34             1           model_1    23    0.89
0khb3459             1           model_2    10    0.67
098uf3n4            -1           model_2    89    0.23
03kjh8ef             1           model_3    16    0.67
...                  ...         ...        ...   ...


I want to visualize the relationships between feedback and the three features model, x1 and x2. I can't think of a way to do this in a statistically correct / intuitive way; what type of visualization would capture most information in this data set in a correct and intuitive way? I don't mind generating several plots (it doesn't all have to be in one plot), so long as they are easy to understand.

So far what I have done is to create the scatter plots below, but they aren't very aesthetic and don't give much insight.

EDIT: One nice bar-plot visualization I have generated did give useful insight into the 3 models, shown below. It just counts the absolute number of times feedback was given for each model.

• It would be helpful if you can describe the data itself and the desired goal of the visualization in a bit more detail. From what you posted here I guess x1 and x2 are continuous and my first suggestion would be boxplots separated by model and/or feedback – Maarten Punt Jan 10 '18 at 21:09
• @MaartenPunt given the sensitivity of the data I am afraid I can't say exactly what x1 and x2 represent, but yes they are both continuous and with ranges [10,160] and [0,1] respectively. The goal of the visualization would be to understand how each model is performing with respect to features x1 and x2. – PyRsquared Jan 11 '18 at 8:45