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Data Mining with Weka -Installation

Weka - Data mining Tool W eka is a tool for big data and data mining. It is used to various classification, experiments, and analysis over large data sets. Installation Guide -weka  You can download Weka from   here   and follow the normal installation procedure. After completion you will get following window, here you can begin your classification or experiment on different data sets with Weka.
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Data Mining With Weka -Experiment (ALL at once- “Adult, Irish, Zoo” & “J48, Naïve Bayes, KNN)

Weka - Data Mining Tool

 Experiment 
(ALL at once- “Adult, Irish, Zoo” & “J48, Naïve Bayes, KNN)
In this experiment I am using all algorithms and datasets at once in order to show the comparison and do the evaluation among the datasets and its correctness including standard deviation.


 Here I am comparing and evaluating the previous results.
v – significantly better, *-significantly worse

Evaluating J48, Naïve Bayes, KNN on Zoo dataset:  We got average of 92.66(SD: 7.07), 93.69(6.99) and 96.06(5.41) percent correct using J48, Naïve Bayes, KNN on zoo dataset. It’s a  10 fold cross validation so, if we want to see individual result we can save result on CVS file from setup panel.

Evaluating J48, Naïve Bayes, KNN on Iris dataset:  We got average of 95.33, 92.60, 86.47  percent correct using J48, Naïve Bayes, KNN on iris dataset. It’s a  10 fold cross validation so, if we want to see individual result we can save result on CVS file from setup panel.

Evaluating J48, Naïve Bayes, KNN on Adult dataset:  We got average of 86.13, 83.45, 79.35 percent correct using J48, Naïve Bayes, KNN on adult dataset. It’s a  10 fold cross validation so, if we want to see individual result we can save result on CVS file from setup panel.


 


Moreover, you can found available result based on the significance level of  0.05 or 5 percentage by default and v represents significantly better result while * represents significantly worse results based on value of significance.
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