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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

Dimension reduction methods - Delta's Aircraft Fleet

Fleet planning is a part of the strategic planning process for any airline company. Fleet is the total number of aircraft that an airline operates, as well as the specific aircraft types that comprise the total fleet. Airline selection criteria for aircraft acquisition are based on technical/performance characteristics, economic and financial impact, environmental regulations and constraints, marketing considerations, and political realities. Fleet composition is a critical long-term strategic decision for an airline company. Each aircraft type has different technical performance characteristics, for example, the capacity to carry the payload over a maximum flight distance or range. It affects financial position, operating costs, and especially the ability to serve specific routes.

Getting ready

In order to perform dimension reduction we shall be using a dataset collected on Delta Airlines Aircraft Fleet.

Step 1 - collecting and describing...

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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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