Update Run_Code.py

Run_Code.py

@@ -1,30 +1,30 @@
+from __future__ import print_function
 from sklearn import tree
 from sklearn.svm import SVC
 from sklearn.linear_model import Perceptron
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.metrics import accuracy_score
 import numpy as np
 
-# Data and labels
-X = [[181, 80, 44], [177, 70, 43], [160, 60, 38], [154, 54, 37], [166, 65, 40], [190, 90, 47], [175, 64, 39],
+# data labels [weight, age, shoe size]
+X = [[181, 80, 44], [177, 70, 43], [160, 60, 38], [154, 54, 37], [166, 65, 40],
+     [190, 90, 47], [175, 64, 39],
      [177, 70, 40], [159, 55, 37], [171, 75, 42], [181, 85, 43]]
 
-Y = ['male', 'male', 'female', 'female', 'male', 'male', 'female', 'female', 'female', 'male', 'male']
+Y = ['male', 'male', 'female', 'female', 'male', 'male', 'female', 'female','female', 'male', 'male']
 
-# Classifiers
-# using the default values for all the hyperparameters
 clf_tree = tree.DecisionTreeClassifier()
 clf_svm = SVC()
 clf_perceptron = Perceptron()
-clf_KNN = KNeighborsClassifier()
+clf_knn = KNeighborsClassifier()
 
-# Training the models
+#training
 clf_tree.fit(X, Y)
 clf_svm.fit(X, Y)
 clf_perceptron.fit(X, Y)
-clf_KNN.fit(X, Y)
+clf_knn.fit(X, Y)
 
-# Testing using the same data
+#testing using same data
 pred_tree = clf_tree.predict(X)
 acc_tree = accuracy_score(Y, pred_tree) * 100
 print('Accuracy for DecisionTree: {}'.format(acc_tree))
@@ -33,15 +33,19 @@
 acc_svm = accuracy_score(Y, pred_svm) * 100
 print('Accuracy for SVM: {}'.format(acc_svm))
 
+#perceptron model
 pred_per = clf_perceptron.predict(X)
 acc_per = accuracy_score(Y, pred_per) * 100
 print('Accuracy for perceptron: {}'.format(acc_per))
 
-pred_KNN = clf_KNN.predict(X)
+pred_KNN = clf_knn.predict(X)
 acc_KNN = accuracy_score(Y, pred_KNN) * 100
 print('Accuracy for KNN: {}'.format(acc_KNN))
 
-# The best classifier from svm, per, KNN
+#choosing the best
 index = np.argmax([acc_svm, acc_per, acc_KNN])
 classifiers = {0: 'SVM', 1: 'Perceptron', 2: 'KNN'}
+
 print('Best gender classifier is {}'.format(classifiers[index]))
+
+#     print(prediction)