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Machine Learning with scikit-learn Quick Start Guide

You're reading from Machine Learning with scikit-learn Quick Start Guide Classification, regression, and clustering techniques in Python

Product type Paperback

Published in Oct 2018

Publisher Packt

ISBN-13 9781789343700

Length 172 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Kevin Jolly

View More author details

Table of Contents (10) Chapters

Preface

1. Introducing Machine Learning with scikit-learn FREE CHAPTER

2. Predicting Categories with K-Nearest Neighbors

3. Predicting Categories with Logistic Regression

4. Predicting Categories with Naive Bayes and SVMs

5. Predicting Numeric Outcomes with Linear Regression

6. Classification and Regression with Trees

7. Clustering Data with Unsupervised Machine Learning

8. Performance Evaluation Methods

9. Other Books You May Enjoy

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Preparing a dataset for machine learning with scikit-learn

The first step to implementing any machine learning algorithm with scikit-learn is data preparation. Scikit-learn comes with a set of constraints to implementation that will be discussed later in this section. The dataset that we will be using is based on mobile payments and is found on the world's most popular competitive machine learning website – Kaggle.

You can download the dataset from: https://www.kaggle.com/ntnu-testimon/paysim1.

Once downloaded, open a new Jupyter Notebook by using the following code in Terminal (macOS/Linux) or Anaconda Prompt/PowerShell (Windows):

Jupyter Notebook

The fundamental goal of this dataset is to predict whether a mobile transaction is fraudulent. In order to do this, we need to first have a brief understanding of the contents of our data. In order to explore the dataset...

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Authors (1)

Jolly

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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