Is it possible to use k-nearest neighbour for classification with more than two attributes? If I were to have a dataset of 9 attributes of different types that describe current weather, such as temperature, humidity, etc., and want to classify the current weather by use of a k-NN algorithm, is this possible?
From what I understand, k-NN has two different attributes that are plotted, and, wherever a point is drawn, its nearest neighbors will classify it.
Could I do the same thing but each data point is placed based on its 9 attributes?
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.html
https://medium.com/@luigi.fiori.lf0303/distance-metrics-and-k-nearest-neighbor-knn-1b840969c0f4