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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

Continuous Markov chains - vehicle service at a gas station

A gas station has a single pump. There is no space for vehicles to wait. If a vehicle arrives at the pump and there is no place the vehicle leaves without filling at the pump. Vehicles arrive at the gas station following a Poisson process with a rate of 3/20 vehicles per minute. Of the vehicles arriving at the pump, 75% are cars and 25% are motorcycles. The refueling time can be modeled with an exponential random variable with a mean of eight minutes for cars and three minutes for motorcycles.

Getting ready

In order to perform continuous Markov chains for vehicle service at a gas station we shall be simulating data.

How to do it...

Let's get into the details.

Step 1 - preparing the dataset

Load the following packages:

    > install.packages("simmer")
    > install.packages("ggplot2")
    > library(simmer)
    > library(ggplot2)

Note

Version info: Code for this...

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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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