Introduction to K-Nearest Neighbors
Explore how to apply the K-Nearest Neighbors algorithm for classification tasks using Python. Understand how KNN predicts outcomes based on the closest data points and learn steps to implement, evaluate, and optimize the model for practical use.
We'll cover the following...
We'll cover the following...
Quick overview
Our next supervised learning classification technique is K-Nearest Neighbors (k-NN), which classifies new unknown data points based on their proximity to known data points. This classification process is determined by setting the k number of data points closest to the target data point. For example, if you set k to 3, k-NN analyzes the nearest three data points (neighbors) to the target data point.
Example
Given the following dataset and predict the class for p ( and ) and value of .
| Sr. | P1 | P2 | Class |
| i | 7 | 7 | B |
| ii | 7 | 4 | B |
| iii | 3 | 4 | A |
| iv | 1 | 4 | A |
- Euclidean distance
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