How to Deal with Missing Number NaN in MATLAB

Dealing with missing values, represented as NaN (Not a Number), is a common challenge when working with data in MATLAB. In this tutorial, we'll explore effective strategies to handle NaN values and ensure accurate data analysis and computations. We'll cover techniques such as identifying and locating NaN values in arrays or datasets, handling missing data in mathematical operations, filtering NaN values, and imputing missing values using various approaches. Whether you're working on data preprocessing, statistical analysis, or machine learning tasks, mastering these techniques will enhance your MATLAB skills and enable you to handle missing data effectively.