  In this article we study variable selection problem using LASSO with new
improvisations. LASSO uses $\ell_{1}$ penalty, it shrinks most of the
coefficients to zero when number of explanatory variables $(p)$ are much larger
the number of observations $(N)$. Novelty of the approach developed in this
article blends basic ideas behind resampling and LASSO together which provides
a significant variable reduction and improved prediction accuracy in terms of
mean squared error in the test sample. Different weighting schemes have been
explored using Bootstrapped LASSO, the basic methodology developed in here.
Weighting schemes determine to what extent of data blending in case of grouped
data. Data sharing (DSL) technique developed by [11] lies at the root of the
present methodology. We apply the technique to analyze the IMDb dataset as
discussed in [11] and compare our result with [11].
