Missing Data consists of the existence of absent values in a dataset and is a common obstacle that data scientists face in real-world domains. Missing data can occur in a variety of applications, for several different reasons, and regardless of whatever they might be, missing data has serious implications for knowledge extraction and classification performance.
For that reason, handling missing data is one important step in Data Preparation.
- 01 - Introduction to Missing Data
- 02 - Missing Data Imputation with Statistical Methods
- 03 - Missing Data Imputation with Machine Learning Methods (👷♀️ coming soon!)