Book Image

Practical Machine Learning Cookbook

By : Atul Tripathi
Book Image

Practical Machine Learning Cookbook

By: Atul Tripathi

Overview of this book

Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.
Table of Contents (21 chapters)
Practical Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
14
Case Study - Forecast of Electricity Consumption

Decision tree learning - income-based distribution of real estate values


Income has been an essential component of the attractive long-term total returns provided by real estate as an asset class. The annual income returns generated from investing in real estate have been more than 2.5 times higher than stocks and lagged bonds by only 50 basis points. Real estate often provides a steady source of income based on the rent paid by tenants.

Getting ready

In order to perform decision tree classification, we will be using a dataset collected from the real estate dataset.

Step 1 - collecting and describing the data

The dataset titled RealEstate.txt will be used. This dataset is available in TXT format, titled RealEstate.txt. The dataset is in standard format. There are 20,640 rows of data. The 9 numerical variables are as follows:

  • MedianHouseValue
  • MedianIncome
  • MedianHouseAge
  • TotalRooms
  • TotalBedrooms
  • Population
  • Households
  • Latitude
  • Longitude

How to do it...

Let's get into the details.

Step 2 - exploring the data...