Why exploratory data analysis is important

The flexibility to present and process insurance data in a manner that is easy to work with is of vital importance. The best machine learning models are built from clean, high-quality data that has been effectively and skilfully processed. Quite often, Bowden said, this task requires the heaviest lifting and has led to a running joke that most data scientists spend 80% of their time cleaning data and only 20% calibrating models. Although the core of EDA involves summary statistics, Bowden stressed that there is often more to it. Understanding the data types is often the first step and identifying which fields will be numerical and which are categorical is the crucial next step.