# Demo Prediction

Sep 17th, 2021
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1. weather = ["Sunny","Sunny","Overcast","Rainy","Rainy","Rainy","Overcast","Sunny","Sunny","Rainy","Sunny","Overcast","Overcast","Rainy"]
2. temp=["Hot","Hot","Hot","Mild", "Cool", "Cool","Cool","Mild", "Cool", "Mild","Mild","Mild","Hot","Mild"]
3. play=["No","No","Yes","Yes","Yes","No","Yes","No","Yes","Yes","Yes","Yes","Yes","No"]
4.
5. from sklearn import preprocessing
6. le = preprocessing.LabelEncoder()
7.
8. #Convert to Binary
9. weather_encoded = le.fit_transform(weather)
10. print(weather_encoded)
11. temp_encoded = le.fit_transform(temp)
12. print(temp_encoded)
13. play_encoded = le.fit_transform(play)
14. print(play_encoded)
15.
16. #convert to tuple
17. feature = tuple(zip(weather_encoded, temp_encoded))
18. print(feature)
19.
20. #naive bayes
21. from sklearn.naive_bayes import GaussianNB
22. model =  GaussianNB()
23. model.fit(feature,play_encoded)
24. result = model.predict([[2,0],[2,1],[1,2]])
25. print("Naive : ",result)
26.
27. #decision tree
28. from sklearn.tree import DecisionTreeClassifier
29. model =  DecisionTreeClassifier()
30. model.fit(feature,play_encoded)
31. result = model.predict([[2,0],[2,1],[1,2]])
32. print(result)
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