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You're reading from  Applied Supervised Learning with Python

Product typeBook
Published inApr 2019
Reading LevelIntermediate
Publisher
ISBN-139781789954920
Edition1st Edition
Languages
Python
Tools
TensorFlow
Concepts
Neural Networks
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Authors (2):
Benjamin Johnston
Benjamin Johnston
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Benjamin Johnston

Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.
Read more about Benjamin Johnston

Ishita Mathur
Ishita Mathur
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Ishita Mathur

Ishita Mathur has worked as a data scientist for 2.5 years with product-based start-ups working with business concerns in various domains and formulating them as technical problems that can be solved using data and machine learning. Her current work at GO-JEK involves the end-to-end development of machine learning projects, by working as part of a product team on defining, prototyping, and implementing data science models within the product. She completed her masters' degree in high-performance computing with data science at the University of Edinburgh, UK, and her bachelor's degree with honors in physics at St. Stephen's College, Delhi.
Read more about Ishita Mathur

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Multiple Linear Regression

We have already covered regular linear regression, as well as linear regression with polynomial terms, and considered training them with both the least squares method and gradient descent. This section of the chapter will consider an additional type of linear regression: multiple linear regression, where more than one type of variable (or feature) is used to construct the model. To examine multiple linear regression, we will use a modified version of the Boston Housing Dataset, available from https://archive.ics.uci.edu/ml/index.php. The modified dataset can be found in the accompanying source code or on GitHub at https://github.com/TrainingByPackt/Supervised-Learning-with-Python and has been reformatted for simplified use. This dataset contains a list of different attributes for property in the Boston area, including the crime rate per capita by town, the percentage of the population with a lower socio-economic status, as well as the average number of rooms per...

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Applied Supervised Learning with Python
Published in: Apr 2019Publisher: ISBN-13: 9781789954920
© 2019 Packt Publishing Limited All Rights Reserved

Authors (2)

author image
Benjamin Johnston

Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.
Read more about Benjamin Johnston

author image
Ishita Mathur

Ishita Mathur has worked as a data scientist for 2.5 years with product-based start-ups working with business concerns in various domains and formulating them as technical problems that can be solved using data and machine learning. Her current work at GO-JEK involves the end-to-end development of machine learning projects, by working as part of a product team on defining, prototyping, and implementing data science models within the product. She completed her masters' degree in high-performance computing with data science at the University of Edinburgh, UK, and her bachelor's degree with honors in physics at St. Stephen's College, Delhi.
Read more about Ishita Mathur

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