You may need to manipulate data to transform it to the required format. The following are some of the frequently used scenarios and modules available.
Clean missing data and missing values in data are probably the most common problems you need to fix before data analysis. When missing values are present, certain algorithms may not work or you may not have the desired result. So, you need to get rid of the missing values either by replacing them with some logical values or by removing the existing row(s) or column(s).
ML Studio comes with a module, Clean Missing Data, to solve this exact problem. It lets you either remove the rows or columns that have missing values or lets you replace the values in the rows and columns with one of the these: mean, median, mode, custom values, a value that uses the probabilistic form of Principal Component Analysis (PCA), or Multiple Imputation by Chained Equations (MICE). MICE is a statistical technique that updates each...
