  We implement machine learning algorithms to nuclear data. These algorithms
are purely data driven and generate models that are capable to capture
intricate trends. Gradient boosted trees algorithm is employed to generate a
trained model from existing nuclear data, which is used for prediction for data
of damping parameter, shell correction energies, quadrupole deformation,
pairing gaps, level densities and giant dipole resonance for large number of
nuclei. We, in particular, predict level density parameter for superheavy
elements which is of great current interest. The predictions made by the
machine learning algorithm is found to have standard deviation from 0.00035 to
0.73.
