Working with nearest-neighbors

We will 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 will calculate a weighted average of the target value.

How to do it...

We proceed with the recipe as follows:

1. 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...