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Chapter 1 In the preceding dataset, we had an obvious feature type. We wanted to know if people are short or tall, so we collected their heights. This engineering feature is an important problem in data mining. In later chapters, we will discuss methods for choosing good features to collect in your dataset. Ultimately, this step often requires some expert domain knowledge or at least some trial and error. In this book, we will introduce data mining through Python. In some cases, we choose clarity of code and workflows, rather than the most optimized way to do this. This sometimes involves skipping some details that can improve the algorithm's speed or effectiveness. Using Python and the IPython Notebook In this section, we will cover installing Python and the environment that we will use for most of the book, the IPython Notebook. Furthermore, we will install the numpy module, which we will use for the first set of examples. Installing Python The Python language is a fantastic, versatile, and an easy to use language. For this book, we will be using Python 3.4, which is available for your system from the Python Organization's website: https://www.python.org/downloads/. There will be two major versions to choose from, Python 3.4 and Python 2.7. Remember to download and install Python 3.4, which is the version tested throughout this book. In this book, we will be assuming that you have some knowledge of programming and Python itself. You do not need to be an expert with Python to complete this book, although a good level of knowledge will help. If you do not have any experience with programming, I recommend that you pick up the Learning Python book from. [ 3 ]
Getting Started with Data Mining The Python organization also maintains a list of two online tutorials for those new to Python: • For nonprogrammers who want to learn programming through the Python language: https://wiki.python.org/moin/BeginnersGuide/ NonProgrammers • For programmers who already know how to program, but need to learn Python specifically: https://wiki.python.org/moin/BeginnersGuide/ Programmers Windows users will need to set an environment variable in order to use Python from the command line. First, find where Python 3 is installed; the default location is C:\Python34. Next, enter this command into the command line (cmd program): set the enviornment to PYTHONPATH=%PYTHONPATH%;C:\Python34. Remember to change the C:\Python34 if Python is installed into a different directory. Once you have Python running on your system, you should be able to open a command prompt and run the following code: $ python3 Python 3.4.0 (default, Apr 11 2014, 13:05:11) [GCC 4.8.2] on Linux Type "help", "copyright", "credits" or "license" for more information. >>> print("Hello, world!") Hello, world! >>> exit() Note that we will be using the dollar sign ($) to denote that a command is to be typed into the terminal (also called a shell or cmd on Windows). You do not need to type this character (or the space that follows it). Just type in the rest of the line and press Enter. [ 4 ]
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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 The classic algorithm for
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Chapter 4 When loading the file, we
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Chapter 4 We will sample our datase
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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 Thought should always be
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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 [18, 19, 20], [21, 22, 23
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Chapter 5 Next, we create our trans
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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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Chapter 5 Putting it all together N
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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 Next, we will need a list
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Chapter 7 Make sure the filename is
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Chapter 7 cursor = results['next_cu
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Chapter 7 Next, we are going to rem
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Chapter 7 Creating a graph Now, we
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Chapter 7 As you can see, it is ver
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Chapter 7 Next, we will only add th
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Chapter 7 The difference in this gr
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Chapter 7 We can graph the entire s
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Chapter 7 Optimizing criteria Our a
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Chapter 7 Next, we need to get the
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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 red lines indicate th
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Chapter 8 The combination of an app
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Chapter 8 Next we set the font of t
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Chapter 8 We can then extract the s
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Chapter 8 Our targets are integer v
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Chapter 8 Then we iterate over our
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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 We can reuse
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Authorship Attribution With our dat
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Authorship Attribution We then reco
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Authorship Attribution If it doesn'
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Authorship Attribution Finally, we
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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 Next, we develop the cod
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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 only fit the X matrix
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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 Finally, we create Thean
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Chapter 11 return [image,] return s
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Chapter 11 Next, we define how the
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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 The final ste
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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 This gives us
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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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