DM4

weather=['Sunny','Sunny','Overcast','Rainy','Rainy','Rainy','Overcast','Sunny','Sunny',
'Rainy','Sunny','Overcast','Overcast','Rainy']
temp=['Hot','Hot','Hot','Mild','Cool','Cool','Cool','Mild','Cool','Mild','Mild','Mild','Hot','Mild']
play=['No','No','Yes','Yes','Yes','No','Yes','No','Yes','Yes','Yes','Yes','Yes','No']

from sklearn import preprocessing

le = preprocessing.LabelEncoder()
.
weather_encoded=le.fit_transform(weather)
print (weather_encoded)

temp_encoded=le.fit_transform(temp)
label=le.fit_transform(play)
print ("Temp:",temp_encoded)
print ("Play:",label)

features=zip(weather_encoded,temp_encoded)
print (features)

from sklearn.naive_bayes import GaussianNB

model = GaussianNB()

model.fit(features,label)

predicted= model.predict([[0,2]]) # 0:Overcast, 2:Mild
print("Predicted Value:", predicted)