To practically demonstrate Nadaraya-Watson estimation, synthetic training data is generated based on a predefined non-linear dependency. By defining the true mathematical function $f(x) = 2\sin(x) + x$, a set of $40$ training examples is computationally created by randomly sampling $x$ values and adding standard normal noise ($\epsilon$) to the outputs, resulting in a noisy dataset suitable for regression.