## Working with Nearest Neighbors

We start this chapter by implementing nearest neighbors to predict housing values. This is a great way to start with nearest neighbors because we will be dealing with numerical features and continuous targets.

### Getting ready

To illustrate how making predictions with nearest neighbors works in TensorFlow, we will use the Boston housing dataset. Here we will be predicting the median neighborhood housing value as a function of several features.

Since we consider the training set the trained model, we will find the k-NNs to the prediction points and do a weighted average of the target value.

### How to do it…

First, we will start by loading the required libraries and starting a graph session. We will use the requests module to load the necessary Boston housing data from the UCI machine learning repository:

import matplotlib.pyplot as plt import numpy as np import tensorflow as tf import requests sess = tf.Session()

Next, we will load the data using the

`requests`

module:housing_url...