Nonparametric kernel estimators are widely used in many research areas of statistics. An important nonparametric kernel estimator of a regression function is the Nadaraya-Watson kernel regression estimator which is often obtained by using a fixed bandwidth. However, the adaptive kernel estimators with varying bandwidths are specially used to estimate density of the long-tailed and multi-mod distributions. In this paper, we consider the adaptive Nadaraya-Watson kernel regression estimators. The results of the simulation study show that the adaptive Nadaraya-Watson kernel estimators have better performance than the kernel estimations with fixed bandwidth.