C4.5

 from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

from sklearn.model_selection import train_test_split

import pandas as pd


data = pd.read_csv("heart.csv") # Load dataset (ensure heart.csv is available)

X, y = data.iloc[:, :-1], data.iloc[:, -1] # Features and target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = DecisionTreeClassifier(criterion="entropy").fit(X_train, y_train) # C4.5 uses entropy

y_pred = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred), "Precision:", precision_score(y_test, y_pred),

      "Recall:", recall_score(y_test, y_pred), "F1 Score:", f1_score(y_test, y_pred))

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