  The presence of a high number of zero flow trades continues to provide a
challenge in identifying gravity parameters to explain international trade
using the gravity model. Linear regression with a logarithmic linear equation
encounters an indefinite value on the logarithmic trade. Although several
approaches to solving this problem have been proposed, the majority of them are
no longer based on linear regression, making the process of finding solutions
more complex. In this work, we suggest a two-step technique for determining the
gravity parameters: first, perform linear regression locally to establish a
dummy value to substitute trade flow zero, and then estimating the gravity
parameters. Iterative techniques are used to determine the optimum parameters.
Machine learning is used to test the estimated parameters by analyzing their
position in the cluster. We calculated international trade figures for 2004,
2009, 2014, and 2019. We just examine the classic gravity equation and discover
that the powers of GDP and distance are in the same cluster and are both worth
roughly one. The strategy presented here can be used to solve other problems
involving log-linear regression.
