Cleaning your dataset
Real-world datasets like the heart disease dataset are often messy, containing duplicated or missing values. In this exercise, you will apply the skills learned in this chapter to perform data cleaning on the heart disease dataset. The dataset has already been loaded for you. Your task is to identify and carry out general cleaning operations based on the EDA results: remove empty columns, drop duplicate rows, and perform imputation on the restecg column, which pertains to an electrocardiogram measure. Pandas has been imported for you as pd, and the heart disease dataset is stored as a pandas DataFrame called heart_disease_df.
This exercise is part of the course
End-to-End Machine Learning
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Drop empty columns
heart_disease_column_dropped = heart_disease_df.____(____, ____)