$1\mathrm{.}1.$A$.$ Preliminary data exploration, understanding and minor corrections in Excel file:$$The starting point for this subtask is the dataset itself, including the data dictionary and preparation$$tasks$/$notes in the second worksheet $($Raw$\_$data$\_$dict$).$The next suggested step is to filter each column in the raw data to see whether there are blanks$($missing values$)$ or obvious data entry errors; the latter should be corrected in the Excel spreadsheet$$already before loading. The anomaly in the column $$Dependents$$ should also be corrected in the Excel$$spreadsheet first, making reasonable assumptions. Finally, add and generate the variables$$Income$\_$total and Loan$\_$Income$\_$ratio as explained in the data dictionary tab.$1\mathrm{.}1.$B$.$ Data preparation for multiple analysis tasks:$$After the initial error removal in the source file $(1.$A$.),$ you now have to perform the following data$$preparation steps in that order:$$ Missing value handling$/$transformation;$$ Data transformation for statistical analysis $($e$.$g$.$ string$/$cat to numeric, one to many$)$;$$ Outlier identification and treatment.$$For this sub$$task$,$ you have essentially three options:$1)$ Use KNIME workflows for each analysis task or all tasks in one single workflow;$2)$ Use only EXCEL or any other tool and load the data for each analysis task;$3)$ Use a combination of the two options above $($e$.$g$.$ create a KNIME workflow to prepare the data$$and write or copy them to Excel files after outlier correction, and then use this for analysis tasks in$$Excel and KNIME; OR: treat missing variables and convert category variables already in Excel do$$outlier analysis only in KNIME.