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Table of Contents Preface ix Chapter 1: Getting Started with Data Mining 1 Introducing data mining 2 Using Python and the IPython Notebook 3 Installing Python 3 Installing IPython 5 Installing scikit-learn 6 A simple affinity analysis example 7 What is affinity analysis? 7 Product recommendations 8 Loading the dataset with NumPy 8 Implementing a simple ranking of rules 10 Ranking to find the best rules 13 A simple classification example 16 What is classification? 16 Loading and preparing the dataset 16 Implementing the OneR algorithm 18 Testing the algorithm 20 Summary 23 Chapter 2: Classifying with scikit-learn Estimators 25 scikit-learn estimators 25 Nearest neighbors 26 Distance metrics 27 Loading the dataset 29 Moving towards a standard workflow 31 Running the algorithm 32 Setting parameters 33 [ i ] www.allitebooks.com
Table of Contents Preprocessing using pipelines 35 An example 36 Standard preprocessing 37 Putting it all together 38 Pipelines 38 Summary 40 Chapter 3: Predicting Sports Winners with Decision Trees 41 Loading the dataset 41 Collecting the data 42 Using pandas to load the dataset 43 Cleaning up the dataset 44 Extracting new features 45 Decision trees 47 Parameters in decision trees 48 Using decision trees 49 Sports outcome prediction 49 Putting it all together 50 Random forests 54 How do ensembles work? 55 Parameters in Random forests 56 Applying Random forests 56 Engineering new features 58 Summary 59 Chapter 4: Recommending Movies Using Affinity Analysis 61 Affinity analysis 61 Algorithms for affinity analysis 62 Choosing parameters 63 The movie recommendation problem 64 Obtaining the dataset 64 Loading with pandas 64 Sparse data formats 65 The Apriori implementation 66 The Apriori algorithm 68 Implementation 69 Extracting association rules 72 Evaluation 76 Summary 79 [ ii ]
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Page 3 and 4: Learning Data Mining with Python Co
Page 5 and 6: About the Author Robert Layton has
Page 7 and 8: Christophe Van Gysel is pursuing a
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Page 13 and 14: Table of Contents Chapter 7: Discov
Page 15 and 16: Table of Contents GPU optimization
Page 18 and 19: Preface If you have ever wanted to
Page 20 and 21: What you need for this book It shou
Page 22 and 23: Preface Reader feedback Feedback fr
Page 24 and 25: Getting Started with Data Mining We
Page 26 and 27: Chapter 1 In the preceding dataset,
Page 28 and 29: After you have the above "Hello, wo
Page 30 and 31: Chapter 1 Windows users may need to
Page 32 and 33: Chapter 1 The dataset we are going
Page 34 and 35: Chapter 1 As an example, we will co
Page 36 and 37: We get the names of the features fo
Page 38 and 39: Chapter 1 Two rules are near the to
Page 40 and 41: Chapter 1 The scikit-learn library
Page 42 and 43: We then iterate over all the sample
Page 44 and 45: Chapter 1 Overfitting is the proble
Page 46: Chapter 1 Summary In this chapter,
Page 49 and 50: Classifying with scikit-learn Estim
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Classifying with scikit-learn Estim
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Classifying with scikit-learn Estim
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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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