Need a step$-$by$-$step guide on how to do this in Weka:

Task $1$: Import the dataset $($segment$-$challenge.arff$)$ you will find it under Weka directory in the data folder for example $($c:$\backslash$Program Files$\backslash$Weka$-3-8\backslash$data$),$ and apply on it the following tasks:

a$)$ Apply three classification algorithms J$48$ decision tree $($use $10$ folds cross validation$),$ MultilayerPerceptron $($using percentage split $),$ and Na$$ve Bayes classifier $($using the training data$)$ then compare and screenshot the classifier error curves and compare the confusion matrices in your report.

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b$)$Apply the SimpleKMeans cluster specifying $6$ classes $($using classes to clusters evaluation mode$)$ and remember to randomize the dataset before applying the clustering technique. Analyze your result good or not good $($reasons$?)$

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Task $2$: Import the $($breast$-$cancer.arff, also under c:$\backslash$Program Files$\backslash$Weka$-3-8\backslash$data$)$ data set and apply the associate Apiori algorithm to extract the top $20$ hidden rules among the data. Give a report to analyzing on your result.