Selected graphical examples with pandas
Though many machine learning algorithms, with appropriately set hyper-parameters, can optimally learn how to map your data with respect to your target outcome, their performance can be further improved by knowing about hidden and subtle problems in data. It is not simply a matter of detecting any missing or outlying case. Sometimes, it is paramount to clarify whether there are any groups or unusual distributions in the data(for instance, multimodal distributions). Clear data plots that explicate the relationship between variables can also lead to the creation of newer and better features that can predict more than the existing ones.
The practice that was just described is called Explorative Data Analysis, which can be effective if it has the following characteristics:
It should be fast, allowing you to explore, develop new ideas and test them, and restart with a new exploration and fresh ideas.
It should be graphic in order to better represent data as...
