from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

​

model = Sequential([

    Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),

    Dense(32, activation='relu'),

    Dense(1, activation='sigmoid')

])

​

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

history = model.fit(X_train_scaled, y_train, epochs=10, batch_size=32, validation_split=0.2)

from sklearn.metrics import classification_report, roc_auc_score

​

y_pred = model.predict(X_test_scaled)

y_pred_class = (y_pred > 0.5).astype(int)

​

print("Classification Report:")

print(classification_report(y_test, y_pred_class))

​

roc_auc = roc_auc_score(y_test, y_pred)

print(f"ROC AUC: {roc_auc}")

import matplotlib.pyplot as plt

from sklearn.metrics import confusion_matrix, roc_curve

​

# Plot training & validation loss values

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'])

plt.title('Model loss')

plt.ylabel('Loss')

plt.xlabel('Epoch')

plt.legend(['Train', 'Validation'], loc='upper right')

plt.show()

​

# Plot training & validation accuracy values

plt.plot(history.history['accuracy'])

plt.plot(history.history['val_accuracy'])

plt.title('Model accuracy')

plt.ylabel('Accuracy')

plt.xlabel('Epoch')

plt.legend(['Train', 'Validation'], loc='lower right')

plt.show()

​

# Plot confusion matrix

cm = confusion_matrix(y_test, y_pred_class)

print("Confusion Matrix:")

print(cm)

​

# Plot ROC curve

fpr, tpr, _ = roc_curve(y_test, y_pred)

plt.plot(fpr, tpr, label=f"ROC curve (AUC = {roc_auc:.2f})")

plt.plot([0, 1], [0, 1], 'k--')

plt.xlabel('False Positive Rate')

plt.ylabel('True Positive Rate')

plt.title('Receiver Operating Characteristic (ROC)')

plt.legend(loc='lower right')

plt.show()