Need to use R.

How to use the dataset to perform Naive Bayes to determine its performance in classifying the variable Class in the dataset.

There is no need to build training and test data sets.Go ahead and use the dataset to both build the model and predict the Class.

Output the confusion matrix.

Choose 1 variable to delete from the 7 available variables and build the model and confusion matrix for the model with 6 remaining variables.

Output the confusion matrix. Should the variable be deleted from the model based on information from the confusion matrix?

I can't post the full dataset, you can make an example by using part of the data.

A B C D E F G H K L Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size Bare.nuclei BI.cromatin Normal.nucleoli Mitoses Class 1 1000025 1 3 1 benign 1002945 10 1 benign 1015425 1 benign 4 1016277 1 benign CO 5 1017023 1 benign 6 1017122 10 1 malignant 7 1018099 1 benign 8 1018561 1 benign 10 9 1033078 5 benign 11 10 1033078 1 benign 12 11 1035283 1 benign 13 12 1036172 1 benign 14 13 1041801 1 malignant 15 1043999 1 benign 16 15 1044572 4 malignant 17 16 1047630 1 malignant 18 17 1048672 1 benign 19 18 1049815 1 benign 20 19 1050670 2 malignant 21 20 1050718 N H O N N NONE N N NE N NN NOONANNOJNNNENNANJNON-N 1 benign 22 21 1054590 4 malignant 23 22 1054593 10 1 malignant 24 23 1056784 1 benign 25 24 1057013 2 NA 1 malignant 26 25 1059552 1 benign 27 26 1065726 1 malignant 28 1066373 1 benign 29 28 1066979 ONWINGWO BOW 1 benign 30 29 1067444 1 benign 31 30 1070935 1 benign 32 31 1070935 1 benign 33 32 1071760 1 benign 34 33 1072179 3 malignant 35 34 1074610 1 benign 36 35 1075123 1 benign 37 36 1079304 1 benign 38 37 1080185 10 1 malignant 39 38 1081791 1 benign 10 39 1084584 10 1 malignant