You're reading from Machine Learning with scikit-learn Quick Start Guide

Product typeBook

Published inOct 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781789343700

Edition1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1)

Kevin Jolly

Summary

This chapter was fundamental in helping you prepare a dataset for machine learning with scikit-learn. You have learned about the constraints that are imposed when you do machine learning with scikit-learn and how to create a dataset that is perfect for scikit-learn.

You have also learned how the k-NN algorithm works behind the scenes and have implemented a version of it using scikit-learn to predict whether a transaction was fraudulent. You then learned how to optimize the parameters of the algorithm using the popular GridSearchCV algorithm. Finally, you have learnt how to standardize and scale your data in order to optimize the performance of your model.

In the next chapter, you will learn how to classify fraudulent transactions yet again with a new algorithm – the logistic regression algorithm!

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Machine Learning with scikit-learn Quick Start Guide

Published in: Oct 2018Publisher: PacktISBN-13: 9781789343700

Author (1)

Kevin Jolly

Kevin Jolly is a formally educated data scientist with a master's degree in data science from the prestigious King's College London. Kevin works as a statistical analyst with a digital healthcare start-up, Connido Limited, in London, where he is primarily involved in leading the data science projects that the company undertakes. He has built machine learning pipelines for small and big data, with a focus on scaling such pipelines into production for the products that the company has built. Kevin is also the author of a book titled Hands-On Data Visualization with Bokeh, published by Packt. He is the editor-in-chief of Linear, a weekly online publication on data science software and products.
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