Efficient k-Nearest Neighbors (k-NN) Solutions with NumPy
<h1>Introduction</h1>
<p>I have a friend who is a city planner. One day, he was tasked with reassessing the location suitability of thousands of gas stations in the city, needing to find the positions of the k-nearest gas stations to each one.</p>
<p>How can we find the nearest k stations with little time? This is a practical application scenario of the k-nearest neighbors problem.</p>
<p>As such, he came to me for help, hoping I could provide a high-performance solution.</p>
<p>So I write down this article and which will guide you on efficiently solving the <a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm" rel="noopener ugc nofollow" target="_blank">k-nearest neighbors</a> problem using NumPy. By comparing it with a Python iterative solution, we will demonstrate the powerful performance of NumPy.</p>
<p><a href="https://medium.com/towards-data-science/efficient-k-nearest-neighbors-k-nn-solutions-with-numpy-58cbac2a0971"><strong>Read More</strong></a></p>