$1)$ Identify the attribute type of each attribute in your dataset $($and provide fully descriptive justification for each of them$)\{$Size$,$ Weight, Sweetness, Crunchiness.........$\}$ as $($nominal$,$ ordinal, interval or ratio etc.$).$ you need to justify why you chose the type. Please see the dataset description below and screenshot. Please explain in details how to determine it in the large dataset using KNIME or I do not necessarily have to use it$,$ and it can be determined by just looking at the data $?2)$ 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. $3)$ Using KNIME, 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. Please explain understandable and step by step. Please do not use the $.$ Dataset Description:

Atribute

Name

Description

$1$ A$\_$id Unique identifier for each fruit

$2$ Size Size of the fruit

$3$ Weight Weight of the fruit

$4$ Sweetness Degree of sweetness of the fruit

$5$ Crunchiness Texture indicatiNodes $>$ Results

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Create metanode

$100\%$

Nodes $>$ Results

column typeng the crunchiness of the fruit

$6$ Juiciness Level of juiciness of the fruit

$7$ Ripeness Stage of ripeness of the fruit

$8$ Acidity Acidity level of the fruit

$9$ Quality Overall quality of the fruit