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Python Machine Learning

You're reading from   Python Machine Learning Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781783555130
Length 454 pages
Edition 1st Edition
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Author (1):
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Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
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Table of Contents (21) Chapters Close

Python Machine Learning
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Giving Computers the Ability to Learn from Data FREE CHAPTER 2. Training Machine Learning Algorithms for Classification 3. A Tour of Machine Learning Classifiers Using Scikit-learn 4. Building Good Training Sets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Training Artificial Neural Networks for Image Recognition 13. Parallelizing Neural Network Training with Theano Index

Exploring the Housing Dataset


Before we implement our first linear regression model, we will introduce a new dataset, the Housing Dataset, which contains information about houses in the suburbs of Boston collected by D. Harrison and D.L. Rubinfeld in 1978. The Housing Dataset has been made freely available and can be downloaded from the UCI machine learning repository at https://archive.ics.uci.edu/ml/datasets/Housing.

The features of the 506 samples may be summarized as shown in the excerpt of the dataset description:

  • CRIM: This is the per capita crime rate by town

  • ZN: This is the proportion of residential land zoned for lots larger than 25,000 sq.ft.

  • INDUS: This is the proportion of non-retail business acres per town

  • CHAS: This is the Charles River dummy variable (this is equal to 1 if tract bounds river; 0 otherwise)

  • NOX: This is the nitric oxides concentration (parts per 10 million)

  • RM: This is the average number of rooms per dwelling

  • AGE: This is the proportion of owner-occupied units...

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