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The number of features is not important to use K-NN algotihm. You have to decide distance measure to detect neighbors. I share with you some links that you can check to see which kinds of distance measures that you can use. Just decide the meause and use your feature vectors in the measure. https://www.kdnuggets.com/2020/11/most-popular-distance-metrics-knn....


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First, let's try to build some intuition for what we mean when we say that we want to "densely cover" a $d$-dimensional space $\mathbb{R}^d$ of real numbers. For simplicity, let's assume that all values in all dimensions are restricted to lie in $[0, 1]$. Even with just a single dimension $d=1$, there are actually already infinitely many different ...


1

This is a rather involved task. What to do from a high-level theoretical perspective might be easy to see, but it's difficult putting that into code from scratch. Doing this in Python using existing libraries in not too complicated, though. See for example this tutorial or this StackOverflow post. Edit: Theoretically, I would first plot (draw) the points ...


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