Up to this point, you have focused on analysing one factor at a time, such as age or marital status. In practice, however, people’s decisions are often influenced by a combination of factors.
For example:
Married individuals with higher education may show less interest in certain banking products. Unmarried individuals with the same education level may show more interest.
In this part, you will learn how to study more than one variable together. This will include using text functions and techniques to join different fields.
You can create new variables by combining existing ones, such as Marital status + Housing situation / Gender + Age group / Loan type + Month
But creating too many combinations can quickly become overwhelming. That’s why a smarter way is to use Pivot Tables, which automatically summarise and group data.
In the next lesson, we will explore how Pivot Tables can simplify this process.