We will use Tableau to look at data preparation and data quality. Though we could also do these activities in R, we will use Tableau since it is a good way of seeing data quality issues and capturing them easily. We can also see problematic issues such as outliers or missing values.
When confronted with many variables, analysts usually start by building a decision tree and then using the variables that the decision tree algorithm has selected with other methods that suffer from the complexity of many variables, such as neural networks. However, decision trees perform worse when the problem at hand is not linearly separable.
In this section, we will use Tableau as a visual data preparation in order to prepare the data for further analysis. Here is a summary of some of the things we will explore:
Looking at columns that do not add any value to the model
Columns that have so many missing categorical values that they do not predict the outcome reliably
Review...
