train test several models

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score, classification_report

results = {}
models = {
    'Random Forest': RandomForestClassifier(random_state=42),
    'Logistic Regression': LogisticRegression(random_state=42),
    'Decision Tree': DecisionTreeClassifier(random_state=42),
    'Gradient Boosting': GradientBoostingClassifier(random_state=42),
    'K-Nearest Neighbors': KNeighborsClassifier(),
}

classification_reports = {}
model_names = []
accuracies = []

for model_name, model in models.items():
    print(f"Training {model_name}...")
    model.fit(X_train, Y_train)

    print(f"Evaluating {model_name}...")
    Y_pred = model.predict(X_test)

    confusion = confusion_matrix(Y_test, Y_pred)
    classification_rep = classification_report(
        Y_test, Y_pred, target_names=['Good', 'Bad'], zero_division=1
    )

    classification_reports[model_name] = classification_rep

    accuracy = accuracy_score(Y_test, Y_pred)

    model_names.append(model_name)
    accuracies.append(accuracy)

    print("\nClassification Report:")
    print(classification_rep)
    print(f"{model_name} Accuracy: {accuracy:.4f}")
    print("=" * 50)