Untitled

# Import necessary libraries
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_text
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Sample dataset: Weather dataset for decision tree classification
data = {
        'Outlook': ['Sunny', 'Sunny', 'Overcast', 'Rain', 'Rain', 'Rain', 'Overcast', 'Sunny', 'Sunny', 'Rain', 'Sunny', 'Overcast', 'Overcast', 'Rain'],
            'Temperature': ['Hot', 'Hot', 'Hot', 'Mild', 'Cool', 'Cool', 'Cool', 'Mild', 'Cool', 'Mild', 'Mild', 'Mild', 'Hot', 'Mild'],
                'Humidity': ['High', 'High', 'High', 'High', 'Normal', 'Normal', 'Normal', 'High', 'Normal', 'Normal', 'Normal', 'High', 'Normal', 'High'],
                    'Windy': [False, True, False, False, False, True, True, False, False, False, True, True, False, True],
                        'PlayTennis': ['No', 'No', 'Yes', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'No']
}

# Convert dataset to DataFrame
df = pd.DataFrame(data)

# Convert categorical variables to numeric using one-hot encoding
X = pd.get_dummies(df[['Outlook', 'Temperature', 'Humidity', 'Windy']])
y = df['PlayTennis'].map({'No': 0, 'Yes': 1})  # Encode target variable

# Split dataset into training and testing sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the decision tree classifier using entropy (ID3 algorithm)
clf = DecisionTreeClassifier(criterion='entropy', random_state=42)
clf.fit(X_train, y_train)

# Evaluate the model
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy of Decision Tree: {accuracy:.2f}")

# Display decision tree rules
tree_rules = export_text(clf, feature_names=list(X.columns))
print("\nDecision Tree Rules:\n", tree_rules)

# Classify a new sample
new_sample = pd.DataFrame([{
        'Outlook_Sunny': 1, 'Outlook_Overcast': 0, 'Outlook_Rain': 0,
            'Temperature_Hot': 0, 'Temperature_Mild': 1, 'Temperature_Cool': 0,
                'Humidity_High': 0, 'Humidity_Normal': 1, 'Windy_False': 1, 'Windy_True': 0
}], columns=X.columns).fillna(0)

# Predict the class for the new sample
prediction = clf.predict(new_sample)
print("\nPrediction for new sample (1: PlayTennis=Yes, 0: PlayTennis=No):", prediction[0])Accuracy of Decision Tree: 1.00

Decision Tree Rules:
|--- Outlook_Overcast <= 0.50
|   |--- Humidity_High <= 0.50
|   |   |--- Windy <= 0.50
|   |   |   |--- class: 1
|   |   |--- Windy >  0.50
|   |   |   |--- Outlook_Rain <= 0.50
|   |   |   |   |--- class: 1
|   |   |   |--- Outlook_Rain >  0.50
|   |   |   |   |--- class: 0
|   |--- Humidity_High >  0.50
|   |   |--- Outlook_Sunny <= 0.50
|   |   |   |--- Windy <= 0.50
|   |   |   |   |--- class: 1
|   |   |   |--- Windy >  0.50
|   |   |   |   |--- class: 0
|   |   |--- Outlook_Sunny >  0.50
|   |   |   |--- class: 0
|--- Outlook_Overcast >  0.50
|   |--- class: 1

Prediction for new sample (1: PlayTennis=Yes, 0: PlayTennis=No): 1Accuracy of Decision Tree: 1.00

Decision Tree Rules:
|--- Outlook_Overcast <= 0.50
|   |--- Humidity_High <= 0.50
|   |   |--- Windy <= 0.50
|   |   |   |--- class: 1
|   |   |--- Windy >  0.50
|   |   |   |--- Outlook_Rain <= 0.50
|   |   |   |   |--- class: 1
|   |   |   |--- Outlook_Rain >  0.50
|   |   |   |   |--- class: 0
|   |--- Humidity_High >  0.50
|   |   |--- Outlook_Sunny <= 0.50
|   |   |   |--- Windy <= 0.50
|   |   |   |   |--- class: 1
|   |   |   |--- Windy >  0.50
|   |   |   |   |--- class: 0
|   |   |--- Outlook_Sunny >  0.50
|   |   |   |--- class: 0
|--- Outlook_Overcast >  0.50
|   |--- class: 1

Prediction for new sample (1: PlayTennis=Yes, 0: PlayTennis=No): 1
}])
}