import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.tree import DecisionTreeClassifier, export_text

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


columns = ["Pregnancies", "Glucose", "BloodPressure", "SkinThickness", 

           "Insulin", "BMI", "DiabetesPedigreeFunction", "Age", "Outcome"]

data = pd.read_csv(r"C:\Users\kvadi\Downloads\diabetes.csv", header=1, names=columns)


X = data.iloc[:, :-1]

y = data.iloc[:, -1]


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

model = DecisionTreeClassifier(criterion='entropy', random_state=42)

model.fit(X_train, y_train)


y_pred = model.predict(X_test)


accuracy = accuracy_score(y_test, y_pred)

precision = precision_score(y_test, y_pred, zero_division=0)

recall = recall_score(y_test, y_pred, zero_division=0)

f1 = f1_score(y_test, y_pred, zero_division=0)


print("Performance Metrics:")

print(f"Accuracy: {accuracy:.2f}")

print(f"Precision: {precision:.2f}")

print(f"Recall: {recall:.2f}")

print(f"F1 Score: {f1:.2f}")

print("\nClassification Report:\n", classification_report(y_test, y_pred))

print("\nConfusion Matrix:\n", confusion_matrix(y_test, y_pred))

print("\nDecision Tree Structure:\n")

print(export_text(model, feature_names=list(X.columns)))

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