In this segment, you will learn about the associations between two categorical variables in a bivariate analysis. Statistical analysis is essential for numerical variables, and it includes different metrics like mean, median, mode, quantiles and boxplots. Here, you will learn how to analyse categorical variables using graphs and charts, and derive maximum insights from them.
In the video, you saw that the positive response of customers to opening a term deposit with the bank increases with the education level. From this, you can infer that the bank should contact people with higher education levels to effectively increase the positive response for opening a term deposit.
Also, based on marital status analysis, you can infer that single individuals have a higher positive response rate. This could be due to various reasons: One reason could be that compared with other categories of customers, single individuals have available income to deposit in long-term savings accounts (term deposit). Hence, the campaign should target single customers.
Another very interesting inference is that people who have not purchased any housing or personal loan are more likely to open a term deposit account with the bank. This is true, probably because people who have already availed loans may not have the necessary funds to invest in a term deposit.
Now, let’s study the association between the age variable and response rate in the next video.
So, age group analysis showed that people in the age group of 60+ or <30 are more likely to respond positively. It may be true for older people, since they want to invest through more secure investment methods such as term deposits to have a secure old age.
From the image above, you can observe that the bank has mostly contacted people in the age group of 30-50, and have made much less contact with people in the age group of 60+ (plot-I), although the chances of getting a positive response are higher from the people who are in the age group of 50+ or 60+ (as shown in plot-II). This is a very important insight that one can draw from this data set, i.e., the bank should target the people in the 50+ age group.
In the next segment, you will learn about multivariate analysis.