Demo Prediction

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()

#Convert to Binary
weather_encoded = le.fit_transform(weather)
print(weather_encoded)
temp_encoded = le.fit_transform(temp)
print(temp_encoded)
play_encoded = le.fit_transform(play)
print(play_encoded)

#convert to tuple
feature = tuple(zip(weather_encoded, temp_encoded))
print(feature)

#naive bayes
from sklearn.naive_bayes import GaussianNB
model =  GaussianNB()
model.fit(feature,play_encoded)
result = model.predict([[2,0],[2,1],[1,2]])
print("Naive : ",result)

#decision tree
from sklearn.tree import DecisionTreeClassifier
model =  DecisionTreeClassifier()
model.fit(feature,play_encoded)
result = model.predict([[2,0],[2,1],[1,2]])
print(result)