Exploratory Data Analysis
Exploratory data analysis, also referred to as EDA, is as important as the other steps in a Data Science project. It helps one to deeply understand the data and capture deviances that can harm the modeling. After all, we know that garbage in will result in garbage out.
There are some steps used to perform data exploration, and that is what we will cover in this chapter. The intent is to go over a practical project, beginning with the dataset load to RStudio until the composition of an EDA report, outlining the most interesting findings. The steps to be covered in this chapter are as follows:
- Loading the dataset to RStudio
- Understanding the data
- Treating missing data
- Exploring and visualizing the data
- Analysis report
