Consider the below Table of parcel dimension, weight, delivery date, temperature and priority status at a post office in London: Reference Temperature Length Width Height Weight ID (K) (cm) (cm) (cm) (kg) 273.15 7.5 10.2 3.2 0.7 288.15 6.3 16.6 -2.8 1.5 333.15 5 16.1 4.2 3 298.15 11.2 5.8 1.3 265.95 4.4 3.3 0.3 265.05 5.2 3.3 285.05 0.1 1.1 AD423 FE472 TG527 MY921 PE692 TG271 TG273 5.2 0.1 0.6 2.8 0.6 Date 01/01/2018 09/01/2024 03/02/2023 ? 6/6/20 09/32/2010 09/09/2012 Temperature Priority (C) Status 0 15 60 25 -7.2 -8.1 11.9 Yes No No Yes Yes Yes Yes (i) Find all values in the Table that require data cleaning and describe how to take care of each. Finally, show the resulting cleaned Table. (ii) A data scientist wants to use the above Table as a dataset for performing the data mining task of predicting the priority status of a post. Therefore the 'Priority Status' column will be used as the class; the rest of the columns are the potential features that the data scientist can use. Which are the (semantically) important features that can be used and which one(s) should not be used and why?