Summary
In this chapter, we applied univariate EDA to a given dataset to plot the distribution of individual features and implemented bivariate analysis to understand the relationship between two features. We also used a correlation heatmap to determine the correlation of the features of the DataFrame. Drawing conclusions from the results of our analyses, we were able to build a statistically probable profile of a high-risk customer most likely to default on a loan.
In the next chapter, we will analyze the medical data of 303 patients and link the data features with the diagnosis of heart disease.