KNN is explained better with the following short example. The objective is to predict the party for which voter will vote based on their neighborhood, precisely geolocation (latitude and longitude). Here we assume that we can identify the potential voter to which political party they would be voting based on majority voters did vote for that particular party in that vicinity so that they have a high probability to vote for the majority party. However, tuning the k-value (number to consider, among which majority should be counted) is the million-dollar question (as same as any machine learning algorithm):

In the preceding diagram, we can see that the voter of the study will vote for Party 2. As within the vicinity, one neighbor has voted for Party 1 and the other voter voted for Party 3. But three voters voted for Party 2. In fact, by this way, KNN solves any given classification problem. Regression problems are solved by taking mean of its neighbors within the given circle or vicinity or k-value.