Software Engineering

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 ·          Experiment type is cross validation by default  ·          And Its classification by default We can also choose other experiment types such as percentage split etc., regression types and we can set the number of

Weka - Data Mining Tool  Algorithms: There are a lot of algorithm in weka for various classification and experiments and some the major and widely used are following : Decision tree(J48): NAME: weka.classifiers.trees.J48 SYNOPSIS: Class for generating a pruned or Un pruned  C4.5 decision tree. Naïve Bayes: NAME: weka.classifiers.bayes.NaiveBayes SYNOPSIS: Class for a Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) KNN(IBK): NAME: weka.classifiers.lazy.IBk SYNOPSIS: K-nearest neighbours classifier. Can select appropriate value of K based on cross-validation. Can also do distance weighting. SVM(LibSVM):  NAME: weka.classifiers.functions.LibSVM SYNOPSIS: A wrapper class for the libsvm tools (the libsvm classes,

Weka - Data Mining Tool Description of Datasets   Description of Adult dataset:   Name: Adult Number of instances: 32561 Number of attributes: 15 Description about attributes:  ·          Age: Type: Numeric,  Missing: 0, Distinct:73 ·          Workclass: Type: Nominal,  Missing: 0, Distinct:9 ·          Fnlwgt: Type: Numeric,  Missing: 0, Distinct:21648 ·          Education: Type: Nominal,  Missing: 0, Distinct:16 ·          Education-num: Type: Numeric,  Missing: 0, Distinct:16 ·          Marital-status: Type: Nominal,  Missing: 0, Distinct:7 ·          Occupation: Type: Nominal,  Missing: 0, Distinct:15 ·          Relationship: Type: Nominal,  Missing: 0, Distinct:6 ·          Race: Type: Nominal,  Missing: 0, Distinct:5 ·          Sex: Type: Nominal,  Missing: 0, Distinct:2 ·          Capital-gain: Type: Numeric,  Missing: 0, Distinct:119 ·          Capital-loss: Type: Numeric,  Missing: 0, Distinct: 92 ·          Hours-per-week: Type: Num

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.