<ul><li>Missing data can cause serious issues in data analysis and machine learning models.</li><li>Examples of missing data include customers not providing income or employment details, missing transaction history, incomplete survey data, and undisclosed medical history with missing lab results.</li><li>Common techniques to handle missing data include removing rows or columns, replacing missing values with specific values like mean or median, estimating missing values based on surrounding data points, and using machine learning models to predict missing values based on other features.</li><li>Each technique has its own advantages and disadvantages, and the choice depends on the nature of the data and the context of the analysis.</li></ul>

The Curious Case of Missing Data — How Do We Handle It?

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