Can you please explain in detail $($step by step$)$ how the below data tasks would be conducted on KNIME using my dataset I have: Tasks

$1$A$.$ Initial data exploration

Identify the attribute type of each attribute in your dataset $\{$Size$,$ Weight, Sweetness, Crunchiness.........$\}$ as $($nominal$,$ ordinal, interval or ratio etc.$).$ If it's unclear, you may need to justify why you chose the type.

Identify the values of the summarising properties for all attributes, including frequency, location and spread $($e$.$g$.$ value ranges of the attributes, frequency of values, distributions, medians, means, variances, percentiles, etc. $-$ the statistics that have been covered in the lectures and materials given$).$ Note that not all of these summary statistics will make sense for all the attribute types, so use your judgement! Where necessary, use proper visualisations for the corresponding statistics.

Using KNIME or other tools, explore your dataset and identify any outliers, clusters of similar instances, "interesting" attributes and specific values of those attributes. Note that you may need to 'temporarily' convert attributes to numeric or from numeric to nominal. The report includes the corresponding snapshots from the tools and an explanation of what has been identified there.