Finding class imbalance

You are focusing on the important preliminary phase of the machine learning lifecycle: Exploratory Data Analysis (EDA).

EDA allows you to better understand the nature of the heart_disease_df dataset, including the relationships between different variables, and potential problems that might need to be addressed before you move on to training your model. Understanding the distribution of classes in your features - for example, patient sex - is a key part of EDA.

Class imbalance, where one class has significantly more samples than another, can potentially bias your model's training process, leading it to favor the majority class.