code: from sklearn import tree
  from sklearn import ensemble
  from sklearn import neighbors
  from sklearn import neural_network
  
  
  #X = [height, weight, shoe size]
  
  X = [[190,80,10],[132,45,6],[189,67,9],[202,100,13],[145,67,6],[154,45,8],[148,89,9],[159,90,7],[188,78,12],[163,80,8]]
  Y =['Female','Male','Male','Male','Female','Female','Male','Female','Female','Male']
  
  # Classifying using Decision Trees
  clf = tree.DecisionTreeClassifier()
  
  clf = clf.fit(X,Y)
  
  prediction = clf.predict([[190,56,8]])
  
  print("Decision tree Result:",prediction)
  
  #Classification using Random Forest
  
  clf2 = ensemble.RandomForestClassifier()
  
  clf2 = clf2.fit(X,Y)
  prediction2 = clf2.predict([[190,56,8]])
  print("Random Forest Result:",prediction2)
  
  #Classification using KNN
  
  clf3 = neighbors.KNeighborsClassifier()
  clf3 = clf3.fit(X,Y)
  
  prediction3 = clf3.predict([[190,56,8]])
  print("KNN Result:",prediction3)
  
  #Classification using neural network
  
  clf4 = neural_network.MLPClassifier()
  clf4 = clf4.fit(X,Y)
  
  prediction4 = clf4.predict([[190,56,8]])
  print("Neural Network Result:",prediction4)
  
  
apis: sklearn.tree.DecisionTreeClassifier sklearn.neighbors.KNeighborsClassifier sklearn.neural_network.MLPClassifier sklearn.ensemble.RandomForestClassifier