from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

# Sample dataset
import pandas as pd

data = {
    'Outlook': ['Sunny','Sunny','Overcast','Rainy','Rainy','Rainy','Overcast','Sunny','Sunny','Rainy','Sunny','Overcast','Overcast','Rainy'],
    'Temperature': ['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']
}

df = pd.DataFrame(data)

# Encode categorical values
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df_encoded = df.apply(le.fit_transform)

X = df_encoded[['Outlook','Temperature']]
y = df_encoded['Play']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = GaussianNB()
model.fit(X_train, y_train)

pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, pred))

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