Data preprocessing and cleaning are essential steps in preparing data for analysis and modeling in MATLAB. These processes involve handling missing values, dealing with outliers, normalizing data, and ensuring data consistency. Let's explore the steps involved in data preprocessing and cleaning in MATLAB:
1. Handling Missing Values:
* Identify Missing Values: MATLAB provides functions like `isnan` and `ismissing` to identify missing values in a dataset. These functions return logical arrays indicating the presence of missing values.
* Imputation: Depending on the nature of missing data, imputation techniques can be used to fill in missing values. MATLAB offers methods like mean imputation (`fillmissing` function), interpolation, or predictive models to estimate missing values based on other available data.
* Removal: In some cases, missing data may be best handled by removing the corresponding rows or columns from the dataset using functions like `rmmissing` or logical indexing.
2. Handling Outliers:
* Outlier Detection: MATLAB provid....
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