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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

Monte Carlo simulations - calibrated Hull and White short-rates

Monte Carlo simulation is a stochastic simulation of system behavior. The simulation uses sampling experiments to be performed on the model and it then conducts numerical experiments using the computer to obtain a statistical understanding of the system behavior.

Getting ready

In order to perform Monte Carlo simulations for calibrated hull and white short-rates, data is taken from sample code that has been shipped with QuantLib 0.3.10, market data used to construct the term structure of interest rates and swaption volatility matrix with corresponding maturities and tenors.

Step 1 - installing the packages and libraries

Load the following packages:

    >install.packages("RQuantLib", type="binary")
    >install.packages("ESGtoolkit")
    >library(RQuantLib)
    >library(ESGtoolkit)

Note

Version info: Code for this page was tested in R version 3.2.2 (2015...

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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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