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Chapter 1 Windows users may need to install the numpy and scipy libraries before installing scikit-learn. Installation instructions are available at www.scipy.org/install.html for those users. Users of major Linux distributions such as Ubuntu or Red Hat may wish to install the official package from their package manager. Not all distributions have the latest versions of scikit-learn, so check the version before installing it. The minimum version needed for this book is 0.14. Those wishing to install the latest version by compiling the source, or view more detailed installation instructions, can go to http://scikit-learn.org/stable/ install.html to view the official documentation on installing scikit-learn. A simple affinity analysis example In this section, we jump into our first example. A common use case for data mining is to improve sales by asking a customer who is buying a product if he/she would like another similar product as well. This can be done through affinity analysis, which is the study of when things exist together. What is affinity analysis? Affinity analysis is a type of data mining that gives similarity between samples (objects). This could be the similarity between the following: • users on a website, in order to provide varied services or targeted advertising • items to sell to those users, in order to provide recommended movies or products • human genes, in order to find people that share the same ancestors We can measure affinity in a number of ways. For instance, we can record how frequently two products are purchased together. We can also record the accuracy of the statement when a person buys object 1 and also when they buy object 2. Other ways to measure affinity include computing the similarity between samples, which we will cover in later chapters. [ 7 ] www.allitebooks.com
Getting Started with Data Mining Product recommendations One of the issues with moving a traditional business online, such as commerce, is that tasks that used to be done by humans need to be automated in order for the online business to scale. One example of this is up-selling, or selling an extra item to a customer who is already buying. Automated product recommendations through data mining are one of the driving forces behind the e-commerce revolution that is turning billions of dollars per year into revenue. In this example, we are going to focus on a basic product recommendation service. We design this based on the following idea: when two items are historically purchased together, they are more likely to be purchased together in the future. This sort of thinking is behind many product recommendation services, in both online and offline businesses. A very simple algorithm for this type of product recommendation algorithm is to simply find any historical case where a user has brought an item and to recommend other items that the historical user brought. In practice, simple algorithms such as this can do well, at least better than choosing random items to recommend. However, they can be improved upon significantly, which is where data mining comes in. To simplify the coding, we will consider only two items at a time. As an example, people may buy bread and milk at the same time at the supermarket. In this early example, we wish to find simple rules of the form: If a person buys product X, then they are likely to purchase product Y More complex rules involving multiple items will not be covered such as people buying sausages and burgers being more likely to buy tomato sauce. Loading the dataset with NumPy The dataset can be downloaded from the code package supplied with the book. Download this file and save it on your computer, noting the path to the dataset. For this example, I recommend that you create a new folder on your computer to put your dataset and code in. From here, open your IPython Notebook, navigate to this folder, and create a new notebook. [ 8 ]
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Page 3 and 4: Learning Data Mining with Python Co
Page 5 and 6: About the Author Robert Layton has
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Page 11 and 12: Table of Contents Preprocessing usi
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Page 18 and 19: Preface If you have ever wanted to
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Predicting Sports Winners with Deci
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Recommending Movies Using Affinity
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Chapter 4 When loading the file, we
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Chapter 4 Implementation On the fir
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Chapter 4 We want to break out the
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The process starts by creating dict
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movie_name_data.columns = ["MovieID
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To do this, we will compute the tes
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Chapter 4 - Train Confidence: 1.000
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Extracting Features with Transforme
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Chapter 5 Other features describe a
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Chapter 5 Similarly, we can convert
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Chapter 5 This returns a different
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Also, we want to set the final colu
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Chapter 5 The downside to transform
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Chapter 5 A transformer is akin to
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We can then create an instance of t
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Social Media Insight Using Naive Ba
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Discovering Accounts to Follow Usin
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Chapter 7 Creating a graph Now, we
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Chapter 7 Optimizing criteria Our a
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• method='nelder-mead': This is u
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Beating CAPTCHAs with Neural Networ
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Chapter 8 The combination of an app
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Chapter 8 Our targets are integer v
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Chapter 8 From these predictions, w
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Chapter 8 This code correctly predi
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The result is shown in the next gra
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Chapter 8 However, it isn't very go
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Chapter 8 Summary In this chapter,
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Authorship Attribution Attributing
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Authorship Attribution If we cannot
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Authorship Attribution After taking
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Authorship Attribution This dataset
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Authorship Attribution "instead", "
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Authorship Attribution Support vect
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Authorship Attribution Kernels When
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Authorship Attribution With our dat
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Authorship Attribution We then reco
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Clustering News Articles In most of
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Chapter 10 API Endpoints are the ac
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The token object is just a dictiona
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Chapter 10 We then create a list to
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Chapter 10 We are going to use MD5
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Chapter 10 We use clustering techni
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Chapter 10 The k-means algorithm is
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Chapter 10 We then print out the mo
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Chapter 10 Our function definition
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Chapter 10 The result from the prec
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Chapter 10 Implementation Putting a
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Chapter 10 Neural networks can also
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We then call the partial_fit functi
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Classifying Objects in Images Using
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Chapter 11 This dataset comes from
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You can change the image index to s
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Chapter 11 Each of these issues has
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Chapter 11 Using Theano, we can def
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Chapter 11 Building a neural networ
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Chapter 11 Getting your code to run
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Chapter 11 Setting up the environme
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This will unzip only one Coval.otf
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Chapter 11 First we create the laye
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Chapter 11 Finally, we set the verb
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Chapter 11 Summary In this chapter,
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Working with Big Data Big data What
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Working with Big Data Governments a
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Working with Big Data We start by c
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Working with Big Data Getting the d
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Working with Big Data If we aren't
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Working with Big Data Before we sta
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Working with Big Data The first val
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Working with Big Data Next, we crea
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Working with Big Data Then, make a
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Working with Big Data Left-click th
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Working with Big Data The result is
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Next Steps… Extending the IPython
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Next Steps… Chapter 3: Predicting
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Next Steps… Vowpal Wabbit http://
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Next Steps… Deeper networks These
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Next Steps… Real-time clusterings
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Next Steps… More resources Kaggle
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authorship, attributing 185-188 AWS
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feature extraction about 82 common
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NetworkX about 145 defining 303 URL
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scikit-learn package references 305
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Thank you for buying Learning Data
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Learning Python Data Visualization
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