from keras import Sequential
from keras.layers import Dense
from keras.optimizers import Adam


X, y = process_dataset()
model = Sequential([
    Dense(16, input_dim=X.shape[1], activation='relu'),
    Dense(16, activation='relu'),
    Dense(1, activation='sigmoid')
])

'''
    Compile the Model
'''
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.01), metrics=['accuracy'])

'''
    Fit the Model
'''
model.fit(X, y, shuffle=True, epochs=1000, batch_size=200, validation_split=0.2, verbose=2)

Epoch 82/1000
 - 0s - loss: 0.2036 - acc: 0.9144 - val_loss: 0.2400 - val_acc: 0.8885
Epoch 83/1000
 - 0s - loss: 0.2036 - acc: 0.9146 - val_loss: 0.2375 - val_acc: 0.8901

Epoch 455/1000
 - 0s - loss: 0.0903 - acc: 0.9630 - val_loss: 0.1317 - val_acc: 0.9417
Epoch 456/1000
 - 0s - loss: 0.0913 - acc: 0.9628 - val_loss: 0.1329 - val_acc: 0.9443