model_data_4.py

# Input
model = Sequential()
# Hidden layer
model.add(Dense(64, kernel_initializer='uniform', input_dim=24, activation='relu'))
# Output layer
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))

# Compiling the model with 'adam' optimizer and loss function as 'binary_crossentropy'
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Training the model
result = model.fit(X_train, y_train, epochs=100, validation_split=0.2, batch_size=40,  verbose=2)