TestModel.java

package org.ml1;



	import java.io.IOException;
	import java.util.Arrays;

	import tech.tablesaw.api.Table;
	import tech.tablesaw.plotly.Plot;
	import tech.tablesaw.plotly.components.Figure;
	import tech.tablesaw.plotly.components.Layout;
	import tech.tablesaw.plotly.traces.BoxTrace;
	import tech.tablesaw.plotly.traces.HistogramTrace;
	import tech.tablesaw.plotly.traces.ScatterTrace;
	import weka.classifiers.Classifier;
	import weka.classifiers.Evaluation;
	import weka.core.Instance;
	import weka.core.Instances;
	import weka.core.converters.ConverterUtils.DataSource;

	public class MedicalInsurance {

		public static Instances getInstances (String filename)
		{
			
			DataSource source;
			Instances dataset = null;
			try {
				source = new DataSource(filename);
				dataset = source.getDataSet();
				dataset.setClassIndex(dataset.numAttributes()-1);
				
				
			} catch (Exception e) {
				// TODO Auto-generated catch block
				e.printStackTrace();
				
			}
			
			return dataset;
		}
		public static void main(String args[]) {
			System.out.println("data analytics");
			try {
			Table insurance_data=Table.read().csv("C:\\Users\\SANDHYA\\Desktop\\dataset2.csv");
			
			System.out.println(insurance_data.shape());
			System.out.println(insurance_data.structure());
			System.out.println(insurance_data.summary());
			Layout l1=Layout.builder().title("age distribution").build();
			//box-plot
			BoxTrace t1=BoxTrace.builder(insurance_data.categoricalColumn("smoker=no"), insurance_data.nCol("charges")).build();
			Plot.show(new Figure(l1,t1));
			//scatter-plot
			ScatterTrace s=ScatterTrace.builder(insurance_data.nCol("bmi"),insurance_data.nCol("charges")).build();
			Plot.show(new Figure(l1,s));
			}
			catch(IOException e) {
				e.printStackTrace();
			}
			Instances train_data = getInstances("C:\\Users\\SANDHYA\\Desktop\\Oracle Project\\train_dataset.arff");
			Instances test_data = getInstances("C:\\Users\\SANDHYA\\Desktop\\Oracle Project\\test_dataset.arff");
			System.out.println(train_data.size());
			
			/** Classifier here is Linear Regression */
			Classifier classifier = new weka.classifiers.functions.Logistic();
			/** */
			try {
				classifier.buildClassifier(train_data);
			} catch (Exception e1) {
				// TODO Auto-generated catch block
				e1.printStackTrace();
			}
			
			
			/**
			 * train the alogorithm with the training data and evaluate the
			 * algorithm with testing data
			 */
			Evaluation eval;
			try {
				eval = new Evaluation(train_data);
				eval.evaluateModel(classifier, test_data);
				System.out.println("** Logistic Regression Evaluation with Datasets **");
				System.out.println(eval.toSummaryString());
				System.out.print(" the expression for the input data as per alogorithm is ");
				System.out.println(classifier);
				
				double confusion[][] = eval.confusionMatrix();
				System.out.println("Confusion matrix:");
				for (double[] row : confusion)
					System.out.println(	 Arrays.toString(row));
				System.out.println("-------------------");

				System.out.println("Area under the curve");
				System.out.println( eval.areaUnderROC(0));
				System.out.println("-------------------");
				
				System.out.println(eval.getAllEvaluationMetricNames());
				
				System.out.print("Recall :");
				System.out.println(Math.round(eval.recall(1)*100.0)/100.0);
				
				System.out.print("Precision:");
				System.out.println(Math.round(eval.precision(1)*100.0)/100.0);
				System.out.print("F1 score:");
				System.out.println(Math.round(eval.fMeasure(1)*100.0)/100.0);
				
				System.out.print("Accuracy:");
				double acc = eval.correct()/(eval.correct()+ eval.incorrect());
				System.out.println(Math.round(acc*100.0)/100.0);
				
				
				System.out.println("-------------------");
			} catch (Exception e1) {
				// TODO Auto-generated catch block
				e1.printStackTrace();
			}
			
			/** Print the algorithm summary */
			
			Instance predicationDataSet = test_data.get(2);
			double value;
			try {
				value = classifier.classifyInstance(predicationDataSet);
				System.out.println("Predicted label:");
				System.out.print(value);
			} catch (Exception e) {
				// TODO Auto-generated catch block
				e.printStackTrace();
			}
			/** Prediction Output */
			
			
		}

	}