When people are trying to learn neural networks with TensorFlow they usually start with the handwriting database. This builds a model that predicts what digit a person has drawn based upon handwriting samples obtained from thousands of persons. To put that into features-labels terms, the combinations of pixels in a grayscale image (white, black, grey) determine what digit is drawn (0, 1, .., 8, 9).

Here we use other data.

(This tutorial is part of our Guide to Machine Learning with TensorFlow & Keras. Use the right-hand menu to navigate.)

Prerequisites

Before reading this TensorFlow Neural Network tutorial, you should first study these three blog posts:

Introduction to TensorFlow and Logistic Regression
What is a Neural Network? Introduction to Neural Networks Part I
Introduction to Neural Networks Part II

Then you need to install TensorFlow. The easiest way to do that on Ubuntu is to follow these instructions and use virtualenv.

Then install Python Pandas, numpy, scikit-learn, and SciPy packages.

The Las Vegas Strip Hotel Dataset from Trip Advisor

Programmers who are learning to using TensorFlow often start with the iris-data database. That given the combination of pixels that show what type of Iris flower is drawn. But we want to do something original here instead of use the Iris dataset. So we will use the Las Vegas Strip Data Set, cited in the paper “Moro, S., Rita, P., & Coelho, J. (2017). Stripping customers’ feedback on hotels through data mining: The case of Las Vegas Strip. Tourism Management Perspectives, 23, 41-52.” and see if we can wrap a neural network around it.

In their paper, the authors wrote a model using the R programming language and used Support Vector Matrices (SVMs) as their algorithm. That is a type of non-linear regression problem. It uses the same approach to solving regular LR problems, which is to find a line that reduces the MSE (mean square error) to its lowest point to build a predictive model. But SVMs take that up a notch in complexity by working with multiple, nonlinear inputs and finds a plane in n-dimensional space and not line on the XY Cartesian Plane.

Here we take the same data and but use a neural network instead of SVM. We will present this in 3 blog posts:

1. Put data into numeric format.
2. Train neural network.
3. Make prediction.

The data and code for this tutorial is located here.