Exploratory Data Analysis (Visual)
Continuing from my previous post on the calculations to do when conducting Exploratory Data Analysis, in this blog post, I am going to discuss how to use visual to explore our data better. To reiterate here, the two main benefits of doing a good EDA is: Have a good understanding of data quality. We need high quality data to build good models. I told most of my students and trainees that data is never clean. We only get it to a quality level that we can use. Gain some quick insights into the project. Understand what are the potential drivers for supervised learning or possible patterns. These insights can be quick-wins to get more buy-in from other stakeholders. Assumptions: In this discussion, I am assuming that the readers have some background on variables (understand what is Categorical Variable or [...]