DS-P6.py

#!/usr/bin/env python
# coding: utf-8

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data = [
 ['Low', 'Low', 2, 'No', 'Yes'],
 ['Low', 'Med', 4, 'Yes', 'Yes'],
 ['Low', 'Low', 4, 'No', 'Yes'],
 ['Low', 'Med', 4, 'No', 'No'],
 ['Low', 'High', 4, 'No', 'No'],
 ['Med', 'Med', 4, 'No', 'No'],
 ['Med', 'Med', 4, 'Yes', 'Yes'],
 ['Med', 'High', 2, 'Yes', 'No'],
 ['Med', 'High', 5, 'No', 'Yes'],
 ['High', 'Med', 4, 'Yes', 'Yes'],
 ['High', 'Med', 2, 'Yes', 'Yes'],
 ['High', 'High', 2, 'Yes', 'No'],
 ['High', 'High', 5, 'Yes', 'Yes']
]


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import pandas as pd
df = pd.DataFrame(data, columns=['Price', 'Maintenance', 'Capacity', 'Airbag', 'Profitable'])


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label_encoders = {}
for column in ['Price', 'Maintenance', 'Airbag', 'Profitable']:
    le = LabelEncoder()
    df[column] = le.fit_transform(df[column])
    label_encoders[column] = le


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X = df.drop(columns=['Profitable'])
y = df['Profitable']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


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clf = DecisionTreeClassifier(criterion='entropy') 
clf.fit(X_train, y_train)


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y_pred = clf.predict(X_test)


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accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)