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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Pages 570 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Atul Tripathi

Table of Contents (21) Chapters

Practical Machine Learning Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Introduction to Machine Learning

2. Classification

3. Clustering

4. Model Selection and Regularization

5. Nonlinearity

6. Supervised Learning

7. Unsupervised Learning

8. Reinforcement Learning

9. Structured Prediction

10. Neural Networks

11. Deep Learning

12. Case Study - Exploring World Bank Data

13. Case Study - Pricing Reinsurance Contracts

14. Case Study - Forecast of Electricity Consumption

Introduction

Decision tree learning - predicting the direction of stock movement

Stock trading is one of the most challenging problems statisticians are trying to solve. There are multiple technical indicators, such as trend direction, momentum or lack of momentum in the market, volatility for profit potential, and volume measures to monitor the popularity in the market, to name a few. These indicators can be used to create strategy to high-probability trading opportunities. Days/weeks/months can be spent discovering the relationships between technical indicators. An efficient and less time-consuming tool, such as a decision tree, can be used. The main advantage of a decision tree is that it is a powerful and easily interpretable algorithm, which gives a good head start.

Getting ready

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

Step 1 - collecting and describing the data

The dataset to be used is the Bank of America's daily closing...

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Authors (1)

Atul Tripathi

Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.

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