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Modeling Real Estate Using Regression Analysis

The real estate market is a type of market where the sales and purchases between sellers and buyers refer to the exchange of real estate of any kind, such as housing, land, commercial premises, and so on. Real estate prices depend on a series of factors that make the asset more palatable for potential buyers. Regression analysis is the statistical process of studying the relationship between a set of independent variables (explanatory variables) and the dependent variable (response variable). Through this technique, it is possible to understand how the value of the response variable changes when the explanatory variable is varied. In this chapter, the real estate market will be modeled through a regression analysis.

In this chapter, we will cover the following topics:

Defining a regression problem
Creating a linear regression model...

Key benefits

Experimental projects showcasing the implementation of high-performance deep learning models with Keras.

Use-cases across reinforcement learning, natural language processing, GANs and computer vision.

Build strong fundamentals of Keras in the area of deep learning and artificial intelligence.

Description

Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.

Who is this book for?

If you are a data scientist, machine learning engineer, deep learning practitioner or an AI engineer who wants to build speedy intelligent applications with minimal lines of codes, then this book is the best fit for you. Sound knowledge of machine learning and basic familiarity with Keras library would be useful.

What you will learn

Apply regression methods to your data and understand how the regression algorithm works

Understand the basic concepts of classification methods and how to implement them in the Keras environment

Import and organize data for neural network classification analysis

Learn about the role of rectified linear units in the Keras network architecture

Implement a recurrent neural network to classify the sentiment of sentences from movie reviews

Set the embedding layer and the tensor sizes of a network

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Nick Nikolov Mar 19, 2019

I want to emphasize first that this is not a book for understanding the concepts deeply. You shouldn't just show up to a data science position and say, "All I know about LSTM layers in recurrent networks is that they have memory like traditional RNN's but they also have gates."However, if you have a mathematical resource that explains the algorithms and model framework, then you will benefit greatly from this book. It's incredibly good at explaining what it is you are doing for each project.

Amazon Verified review

y. b. Apr 19, 2019

This book attracts by some interesting examples. But it is poorly written. Conceptual part has no depth and often is unrelated to the project, really. Code is buggy and does not show that the author even understand what he is doing in the code. For example, in thefashion-mnist project input shape is screwed up and the chapter shows results of 1000 epochs with this network; in chapter on RNN network uses metric 'acc' for prediction of real-valued label.

Keras 2.x Projects: 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras

What do you get with Print?

Keras 2.x Projects

Modeling Real Estate Using Regression Analysis

Defining a regression problem

Creating a linear regression model

Multiple linear regression concepts

Neural networks for regression using Keras

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