Handling Missing Data in a Dataset Using MATLAB

When working with a dataset in MATLAB, it is crucial to properly handle missing data to ensure accurate analysis and results. Here are some common methods to deal with missing data:

1. Removing Rows: One approach is to simply remove any rows that contain missing values. This can be done using the rmmissing function in MATLAB.
2. Imputation: Imputation involves filling in missing values with estimated or calculated values. MATLAB offers various imputation techniques such as mean, median, or mode imputation using functions like fillmissing.
3. Interpolation: Interpolation is another method where missing values are estimated based on the values of neighboring data points. MATLAB provides interpolation functions like interp1 for this purpose.
4. Machine Learning Techniques: Advanced techniques such as using machine learning algorithms to predict missing values based on other features in the dataset can also be employed in MATLAB.

By appropriately handling missing data in a dataset, you can improve the reliability and accuracy of your analysis in MATLAB.