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

Modelling SP 500


The value of the stocks of the 500 largest corporations by market capitalization listed on the New York Stock Exchange or Nasdaq Composite is measured by the S&P 500. Standard & Poor's provides a quick look at the movement of the stock market and economy on the basis of stock prices. The S&P 500 index is the most popular measure used by the financial media and professionals. The S&P 500 index is calculated by taking the sum of the adjusted market capitalization of all S&P 500 stocks and then dividing it with an index divisor developed by Standard & Poor's. The divisor is adjusted when there are stock splits, special dividends, or spinoffs that could affect the value of the index. The divisor ensures that these non-economic factors do not affect the index.

Getting ready

In order to model the S&P 500 index using neural networks, we shall be using a dataset collected from the GSPC dataset.

Step 1 - collecting and describing data

The dataset to be used...