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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.

Data Mining With Weka - Experiment (Irish Dataset) & J48

Weka - Data Mining Tool   Experiment  (Irish Dataset): In this experiment I am using Irish dataset and different algorithm to show classification using 10 fold cross-validation methods, there will be 10 repetitions on the processes to determine the results. (Note: Use of all other datasets and algorithms is similar process).  For beginners, start Weka and click on Experimenter Option 1          Using J48      Here I am using J48 algorithm to Irish datasets, the process is as follows: There are three panels starting with Setup Panel: ·          Click New to start new experiment ·          Click add new under datasets in order to add new dataset i.e. Irish.arff ·            Click add new under Algorithm in order to add new dataset i.e. J48 ·      ...

Data Mining With Weka -DataSets

Weka - Data Mining Tool Data Sets You can download datasets from  Here   (eg. Adult, Irish, Zoo etc used here) and save the datasets in to “.arff” format. All the datasets are in numeric value format so I changed it into Nominal values in order to process by algorithms used in Weka by following process:          Click Open file in order to open dataset file eg. Adult.arff          Select Choose > Filter > Unsupervised >attribute> NumericToNominal          Click All to apply change to all  attributes          Click Apply          See the Result > Type: Nominal         Click Save to save the result eg. Adult.arff Furthermore, we can found number of attribute as 15, and instances as 32562 and Relation as conversion to Nominal. Same Procedure applied to all data sets for eg: In irish , we can found ...