Answer to Question #232000 in Java | JSP | JSF for Zia

Question

You will be tasked to write this program in either C++ or Java, whichever you feel more comfortable with.
The goal is for the program to be able to guess the user's political party before they reach the end
of the survey. This will require your program to gather a substantial amount of data before it can
make accurate guesses. In particular:
1. You should begin by presenting the user with questions that contain answer options that dier based on their political beliefs.
Ex. What should the government do to help the poor?

A. Make it easier to apply for assistance.
B. Put more money into schools.
C. Incentivize welfare.
D. Nothing.

Answer 1

import java.io.*;
import weka.*;
 
public class Prediction {
	public static BufferedReader readDataFile(String file) {
		BufferedReader inp = null;
 
		try {
			inp = new BufferedReader(new FileReader(file));
		} catch (FileNotFoundException e) {
			System.err.println(e);
		}
 
		return inp;
	}
 
	public static Evaluation classify(Classifier model,
			Instances trainingSet, Instances testingSet) throws Exception {
		Evaluation eval = new Evaluation(trainingSet);
 
		model.buildClassifier(trainingSet);
		eval.evaluateModel(model, testingSet);
 
		return eval;
	}
 
	public static double calculateAccuracy(FastVector preds) {
		double ct = 0;
 
		for (int i = 0; i < preds.size(); i++) {
			NominalPrediction np = (NominalPrediction) preds.elementAt(i);
			if (np.predicted() == np.actual()) {
				ct++;
			}
		}
 
		return 100 * ct / preds.size();
	}
 
	public static Instances[][] crossValidationSplit(Instances data, int numberOfFolds) {
		Instances[][] split = new Instances[2][numberOfFolds];
 
		for (int i = 0; i < numberOfFolds; i++) {
			split[0][i] = data.trainCV(numberOfFolds, i);
			split[1][i] = data.testCV(numberOfFolds, i);
		}
 
		return split;
	}
 
	public static void main(String[] args) throws Exception {
		BufferedReader datafile = readDataFile("elections.txt");
 
		Instances data = new Instances(datafile);
		data.setClassIndex(data.numAttributes() - 1);
		Instances[][] split = crossValidationSplit(data, 10);
		Instances[] trainingSplits = split[0];
		Instances[] testingSplits = split[1];
 
		Classifier[] models = { 
				new J48(), 
				new PART(), 
				new DecisionTable(),
				new DecisionStump() 
		};
 
		for (int j = 0; j < models.length; j++) {
 
			FastVector preds = new FastVector();
 
			for (int i = 0; i < trainingSplits.length; i++) {
				Evaluation validation = classify(models[j], trainingSplits[i], testingSplits[i]);
 
				preds.appendElements(validation.preds());
			}
 
			double accuracy = calculateAccuracy(preds);
 
			System.out.println("Accuracy of " + models[j].getClass().getSimpleName() + ": "
					+ String.format("%.2f%%", accuracy)
					+ "\n---------------------------------");
		}
 
	}
}