Creating a Real-Estate Price Prediction Mobile App

In the previous chapter, we covered the theoretical fundamentals; this chapter, on the other hand, will cover the setup of all the tools and libraries.

First, we are going to set up our environment to build a Keras model to predict house prices with real estate data. Then we are going to serve this model using a RESTful API built using Flask. Next, we will set up our environment for Android and create an app that will consume this RESTful API to predict the house price based on features of real estate. Finally, we will repeat the same exercise for iOS. 

The focus of this chapter is on the setup, tools, libraries, and exercising the concepts learned in Chapter 1, Artificial Intelligence Concepts and Fundamentals. The use case is designed to be simple, yet adaptable enough to accommodate similar use-cases. By the end of the chapter, you will be comfortable creating a mobile app for prediction or classification use cases.

In this chapter, we will cover the following topics:

• Setting up the artificial intelligence environment
• Building an ANN model for prediction using Keras and Tenserflow
• Serving the model as an API
• Creating an Android app to predict house prices
• Creating an iOS app to predict house prices