# How to improve deep learning model having less data

I have trained a deep learning model for regression. The accuracy of the model is poor. I am quite new to deep learning. How can I improve it? The target variable Y is obtained by multiplying the features X1 and X2.

DataSet(5800 rows)

X1       |      X2      | Y
1.000000    70.000000   70.000000
0.714286    29.045455   20.746753
0.000000    35.000000   0.000000
0.538462    22.071429   11.884615
0.000000    54.000000   0.000000


Model

#Define a larger model
def larger_model():
#Create Model
model = Sequential()

#Compile Model
return model

#Evaluate Model

estimator = KerasRegressor(build_fn=larger_model, epochs=10, batch_size=5)
kfold = KFold(n_splits=10)
results = cross_val_score(estimator, X, y, cv=kfold)
print("Results: %.5f (%.5f) MSE" % (results.mean(), results.std()))


Output

Results: -83.81452 (170.38108) MSE