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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook Supervised and unsupervised machine learning simplified

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Length 570 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Machine Learning

Author (1):

Atul Tripathi

View More author details

Table of Contents (15) Chapters

Preface

1. Introduction to Machine Learning FREE CHAPTER

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

Exploring World Bank data

13. Case Study - Pricing Reinsurance Contracts

Pricing reinsurance contracts

14. Case Study - Forecast of Electricity Consumption

Random forest - currency trading strategy

The goal of forecasting future price trends for forex markets can be scientifically achieved after carrying out technical analysis. Forex traders develop strategies based on multiple technical analyses such as market trend, volume, range, support and resistance levels, chart patterns and indicators, as well as conducting a multiple time frame analysis using different time-frame charts. Based on statistics of past market action, such as past prices and past volume, a technical analysis strategy is created for evaluating the assets. The main goal for analysis is not to measure an asset's underlying value but to calculate future performance of markets indicated by the historical performance.

Getting ready

In order to perform random forest, we will be using a dataset collected from the US Dollar and GB Pound dataset.

Step 1 - collecting and describing the data

The dataset titled PoundDollar.csv will be used. The dataset is in standard format. There are 5...

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Authors (1)

Atul Tripathi

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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