Learning Data Mining with Python

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Predicting Sports Winners with Decision Trees In this chapter, we will look at predicting the winner of sports matches using a different type of classification algorithm: decision trees. These algorithms have a number of advantages over other algorithms. One of the main advantages is that they are readable by humans. In this way, decision trees can be used to learn a procedure, which could then be given to a human to perform if needed. Another advantage is that they work with a variety of features, which we will see in this chapter. We will cover the following topics in this chapter: • Using the pandas library for loading and manipulating data • Decision trees • Random forests • Using real-world datasets in data mining • Creating new features and testing them in a robust framework Loading the dataset In this chapter, we will look at predicting the winner of games of the National Basketball Association (NBA). Matches in the NBA are often close and can be decided in the last minute, making predicting the winner quite difficult. Many sports share this characteristic, whereby the expected winner could be beaten by another team on the right day. [ 41 ]
Predicting Sports Winners with Decision Trees Various research into predicting the winner suggests that there may be an upper limit to sports outcome prediction accuracy which, depending on the sport, is between 70 percent and 80 percent accuracy. There is a significant amount of research being performed into sports prediction, often through data mining or statistics-based methods. Collecting the data The data we will be using is the match history data for the NBA for the 2013-2014 season. The website http://Basketball-Reference.com contains a significant number of resources and statistics collected from the NBA and other leagues. To download the dataset, perform the following steps: 1. Navigate to http://www.basketball-reference.com/leagues/NBA_2014_ games.html in your web browser. 2. Click on the Export button next to the Regular Season heading. 3. Download the file to your data folder and make a note of the path. This will download a CSV (short for Comma Separated Values) file containing the results of the 1,230 games in the regular season for the NBA. CSV files are simply text files where each line contains a new row and each value is separated by a comma (hence the name). CSV files can be created manually by simply typing into a text editor and saving with a .csv extension. They can also be opened in any program that can read text files, but can also be opened in Excel as a spreadsheet. We will load the file with the pandas (short for Python Data Analysis) library, which is an incredibly useful library for manipulating data. Python also contains a built-in library called csv that supports reading and writing CSV files. However, we will use pandas, which provides more powerful functions that we will use later in the chapter for creating new features. For this chapter, you will need to install pandas. The easiest way to install it is to use pip3, as you did in Chapter 1, Getting Started with Data Mining to install scikit-learn: $pip3 install pandas If you have difficulty in installing pandas, head to their website at http://pandas.pydata.org/getpandas.html and read the installation instructions for your system. [ 42 ]
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[ 1 ] www.allitebooks.com
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Learning Data Mining with Python Co
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About the Author Robert Layton has
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Christophe Van Gysel is pursuing a
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www.allitebooks.com
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Table of Contents Preprocessing usi
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Page 18 and 19: Preface If you have ever wanted to
Page 20 and 21: What you need for this book It shou
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Page 24 and 25: Getting Started with Data Mining We
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Page 40 and 41: Chapter 1 The scikit-learn library
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Page 44 and 45: Chapter 1 Overfitting is the proble
Page 46: Chapter 1 Summary In this chapter,
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Page 86 and 87: Chapter 4 The classic algorithm for
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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 Data Mining with Python

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