Please I need a full answer to the following question quickly:

Load the soybean diagnosis data set in Weka (found in Weka-3.6/data/soybean.arff), then perform the following:

a) Build a decision tree by selecting J48 as the classifier and 10-way cross-validation. Then fill out the following table:

Correctly Classified Instances
Incorrectly Classified Instances
Kappa statistic
Mean absolute error
Root mean squared error
Relative absolute error
Root relative squared error
Total Number of Instances

b) Build a Nave Bayes classifier and select 10-way cross-validation. Then fill out the following table:

Correctly Classified Instances
Incorrectly Classified Instances
Kappa statistic
Mean absolute error
Root mean squared error
Relative absolute error
Root relative squared error
Total Number of Instances

c) Compare between results in previous two sections (a and b), which algorithm give the better result and why?

Please no handwritten answer, it is for the Data Mining subject

Thanks in advance