The Transform menu allows users to clean and transform their data with a comprehensive set of data handling utilities including categorizing data and handling missing values.
- Use Missing Data Handling to detect missing values in the data set and handle them in a specified way. An observation is considered to be missing data if the cell is empty or contains an invalid formula. It is also possible to treat cells containing specific data as missing.
- Use Transform Continuous Data to rescale your data or group your data into classes.
- Use Transform Categorical Data to create dummy variables, convert a string variable in to a numeric, categorical variable, or reduce the number of categories.
- Use Principal Components to reduce the number of features in your dataset while retaining as much of the original variability in the data as possible.