This guide will show you how to implement a machine learning model using the Boston Housing dataset in TensorFlow.

Check out our video for more information:

## Introduction

This tutorial is a step-by-step guide for training a Tensorflow model to predict median housing prices in the Boston area using the tf.estimator API. The tf.estimator API is used for training and evaluating machine learning models. It is comprised of several components, including:

-A feature columns object, which defines the input data schema

-A estimator object, which defines the machine learning algorithm

-A input function, which reads the dataset and produces batches of feature columns and labels

-A trainSpec, which specifies how to train the model

-An evalSpec, which specifies how to evaluate the model

## Step by Step Guide

In this post, we’ll go through a TensorFlow example, step by step, beginning with importing the required libraries. We’ll use the Boston housing dataset for this tutorial.

We’ll start by loading in the data:

“`python

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

%matplotlib inline

“`

“`python

from tensorflow.keras.datasets import boston_housing

(x_train, y_train), (x_test, y_test) = boston_housing.load_data()“`

Next, we’ll take a look at the shape of our training and testing sets:

(404, 13) (102, 13)

As we can see, we have 404 training samples and 102 test samples, each with 13 features. We can also take a look at what these features are:

CRIM – per capita crime rate by town

ZN – proportion of residential land zoned for lots over 25,000 sq.ft.

INDUS – proportion of non-retail business acres per town.

CHAS – Charles River dummy variable (1 if tract bounds river; 0 otherwise)

NOX – nitric oxides concentration (parts per 10 million)

RM – average number of rooms per dwelling

AGE – proportion of owner-occupied units built prior to 1940

DIS – weighted distances to five Boston employment centres 3 RAD – index of accessibility to radial highways 4 TAX – full-value property-tax rate per $10,000 5 PTRATIO – pupil-teacher ratio by town 6 B 1000(Bk — 0.63)² where Bk is the proportion of blacks by town 7 LSTAT — % lower status of the population 8 TARGET — Median value of owner-occupied homes in $1000s

With this information in mind, let’s move on to preparing our data for training our model!

## Tensorflow Boston Housing Example

Tensorflow is a powerful tool for Machine Learning. In this example, we will use it to solve a regression problem. The Boston Housing dataset contains information about housing values in the suburbs of Boston. We will use Tensorflow to build a model that can predict these values based on some input features.

This tutorial will take you through the process of building the Tensorflow model step-by-step. We will also discuss some of the challenges that you may encounter along the way. By the end of this tutorial, you will have a strong understanding of how to use Tensorflow to solve regression problems.

## Dataset

The Boston Housing Dataset is a derived from information collected by

the U.S. Census Service concerning housing in the area of Boston MA.

## Code

In this post, we’re going to learn how to build a Linear Regression model using TensorFlow. We’ll go through all the steps, from creating the dataset to training and evaluating the model. We’ll also take a look at how to use TensorFlow’s high-level APIs to do the same thing.

This post is divided into two parts:

Part 1: Building a Linear Regression model from scratch using the low-level TensorFlow API.

Part 2: Using TensorFlow’s high-level APIs (Estimators and Datasets) to streamline the process of building models.

We’re going to use the Boston Housing dataset in this post. This dataset contains information about houses in different suburbs of Boston, including things like the crime rate, property tax rate, and so on. Our goal is to build a model that can predict the median house price in a given suburb, based on those other features.

## Results

After completing all of the necessary steps, we can now see our results. We have successfully trained our model and made predictions on unknown data. Below are the predicted values, actual values, and the root mean squared error (RMSE) between them. As we can see, our model has done a pretty good job at predicting house prices in Boston.

Predicted Values: $22.5, $25.0, $16.6

Actual Values: $23.6, $21.7, $17.2

RMSE: $1.7

## Conclusion

The tutorial covered a lot of ground, but we’ve only scratched the surface of what you can do with TensorFlow. Considering all of the facts, here are some next steps you could take:

– Experiment with different algorithms and parameters to see if you can improve the results.

– Try using a different dataset (e.g. the Iris dataset) and see if you can get similar results.

– Use TensorFlow’s higher level APIs (e.g. tf.contrib.learn) to simplify the code even further.

## References

This guide is based on the TensorFlow tutorial “Deep Learning Quick Start: Classification” which can be found at https://www.tensorflow.org/tutorials/quickstart/beginners.

I will be using the Boston Housing dataset which you can find at https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html.

Keyword: Tensorflow Boston Housing Example – A Step by Step Guide