import java.util.ArrayList;

    import weka.classifiers.Classifier;
    import weka.core.Attribute;
    import weka.core.DenseInstance;
    import weka.core.Instances;
    import weka.core.Utils;


    public class UseModelWithData {

      public static void main(String[] args) throws Exception {
        // load model
        String rootPath = "G:/";
        Classifier classifier = (Classifier) weka.core.SerializationHelper.read(rootPath+"j48.model");

        // create instances
        Attribute attr1 = new Attribute("age");
        Attribute attr2 = new Attribute("menopause");
        Attribute attr3 = new Attribute("tumor-size");
        Attribute attr4 = new Attribute("inv-nodes");
        Attribute attr5 = new Attribute("node-caps");
        Attribute attr6 = new Attribute("deg-malig");
        Attribute attr7 = new Attribute("breast");
        Attribute attr8 = new Attribute("breast-quad");
        Attribute attr9 = new Attribute("irradiat");
        Attribute attr10 = new Attribute("Class");

        ArrayList<Attribute> attributes = new ArrayList<Attribute>();
        attributes.add(attr1);
        attributes.add(attr2);
        attributes.add(attr3);
        attributes.add(attr4);
        attributes.add(attr5);
        attributes.add(attr6);
        attributes.add(attr7);
        attributes.add(attr8);
        attributes.add(attr9);
        attributes.add(attr10);

        // predict instance class values
        Instances testing = new Instances("Test dataset", attributes, 0);

        // add data
        double[] values = new double[testing.numAttributes()];
        values[0] = testing.attribute(0).addStringValue("60-69");
        values[1] = testing.attribute(1).addStringValue("ge40");
        values[2] = testing.attribute(2).addStringValue("10-14");
        values[3] = testing.attribute(3).addStringValue("15-17");
        values[4] = testing.attribute(4).addStringValue("yes");
        values[5] = testing.attribute(5).addStringValue("2");
        values[6] = testing.attribute(6).addStringValue("right");
        values[7] = testing.attribute(7).addStringValue("right_up");
        values[8] = testing.attribute(0).addStringValue("yes");
        values[9] = Utils.missingValue();

        // add data to instance
        testing.add(new DenseInstance(1.0, values));
        // instance row to predict
        int index = 10;
        // perform prediction
        double myValue = classifier.classifyInstance(testing.instance(10));
        // get the name of class value
        String prediction = testing.classAttribute().value((int) myValue);

        System.out.println("The predicted value of the instance ["
            + Integer.toString(index) + "]: " + prediction);

      }

    }