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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

Multinomial logistic regression - understanding program choices made by students

Let's assume that high school students are to be enrolled on a program. The students are given the opportunity to choose programs of their choice. The choices of the students are based on three options. These choices are general program, vocational program, and academic program. The choice of each student is based on each student's writing score and social economic status.

Getting ready

In order to complete this recipe we shall be using a student's dataset. The first step is collecting the data.

Step 1 - collecting data

The student's dataset titled hsbdemo is being utilized. The dataset is available at: http://voia.yolasite.com/resources/hsbdemo.csv in an MS Excel format. There are 201 data rows and 13 variables in the dataset. The eight numeric measurements are as follows:

id
read
write
math
science
socst
awards
cid

The non-numeric measurements are as follows:

gender
ses
schtyp
prog
honors

How to do it...

Let's get into the...

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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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