How to use basic classifiers

classifiers.py

from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import RandomForestClassifier
 
#{height, weights, shoe size}
X = [[190,70,44],[166,65,45],[190,90,47],[175,64,39],[171,75,40],[177,80,42],[160,60,38],[144,54,37]]
Y = ['male','male','male','male','female','female','female','female']
 
#Predict for this vector (height, wieghts, shoe size)
P = [[190,80,46]]

print(type(X), type(Y))
 
#{Decision Tree Model}
clf = DecisionTreeClassifier()
clf = clf.fit(X,Y)
print ("\n1) Using Decision Tree Prediction is" + str(clf.predict(P)))
 
#{K Neighbors Classifier}
knn = KNeighborsClassifier()
knn.fit(X,Y)
print ("2) Using K Neighbors Classifier Prediction is " + str(knn.predict(P)))
 
#{using MLPClassifier}
mlpc = MLPClassifier()
mlpc.fit(X,Y)
print ("3) Using MLPC Classifier Prediction is " + str(mlpc.predict(P)))
 
#{using MLPClassifier}
rfor = RandomForestClassifier()
rfor.fit(X,Y)
print ("4) Using RandomForestClassifier Prediction is " + str(rfor.predict(P)) +"\n")

A basic example of how to use different classifiers.