X_train_sample,
Y_train_sample,
cv=kfold)
print("Model: %.2f (%.2f) MSE" % (results.mean(), results.std()))
# evaluate baseline model with standardized dataset
run_model(baseline_model, 50, 8)
run_model(larger_model, 50, 8)
run_model(wider_model, 50, 8)
X_train_sample = X_train.sample(n=100000, random_state=42)
Y_train_sample = Y_train[X_train_sample.index]
model = wider_model()
model.summary()
model.fit(X_train_sample,
Y_train_sample,
epochs=30,
batch_size=16,
validation_split=0.2)
Y_pred = model.predict(X_valid).clip(0, 20)
MSE = mean_squared_error(Y_valid, Y_pred)
MSE
Y_test = model.predict(X_test).clip(0, 20)
# Prepare for submission
