  Compositional data arise in many real-life applications and versatile methods
for properly analyzing this type of data in the regression context are needed.
When parametric assumptions do not hold or are difficult to verify,
non-parametric regression models can provide a convenient alternative method
for prediction. To this end, we consider an extension to the classical
$k$--$NN$ regression, termed $\alpha$--$k$--$NN$ regression, that yields a
highly flexible non-parametric regression model for compositional data through
the use of the $\alpha$-transformation. Unlike many of the recommended
regression models for compositional data, zeros values (which commonly occur in
practice) are not problematic and they can be incorporated into the proposed
models without modification. Extensive simulation studies and real-life data
analyses highlight the advantage of using these non-parametric regressions for
complex relationships between the compositional response data and Euclidean
predictor variables. Both suggest that $\alpha$--$k$--$NN$ regression can lead
to more accurate predictions compared to current regression models which assume
a, sometimes restrictive, parametric relationship with the predictor variables.
In addition, the $\alpha$--$k$--$NN$ regression, in contrast to current
regression techniques, enjoys a high computational efficiency rendering it
highly attractive for use with large scale, massive, or big data.
