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example 2 features 2 follower information, obtaining from Twitter 140, 141 graph, creating 145-147 image, splitting into individual letters 167-169 loading 41, 136, 137, 251 loading, pandas used 43 network, building 142-144 new features, extracting 45, 46 references 301 samples 2 similarity graph, creating 147-151 training dataset, adjusting to methodology 171 training dataset, creating 169, 170 URL 42 decision tree implementation min_samples_leaf 48 min_samples_split 48 decision trees about 47, 48 advantages 41 Gini impurity 48 Information gain 48 parameters 48 using 49 dictionary improved prediction function, testing 181, 182 ranking mechanisms, for words 180 used, for improving accuracy 180 DictVectorizer class 127 disambiguation about 106 data, downloading from social network 107, 108 dataset, classifying 109-114 dataset, loading 109-114 replicable dataset, creating from Twitter 114-117 discretization algorithm defining 17 documents attributing, to authors 186 E EC2 service console URL 260 Eclat algorithm about 63 implementing 301 URL 301 Elastic Map Reduce (EMR) 293 Enron dataset accessing 201 classifier, using 206 dataset loader, creating 201-205 evaluation 207-209 existing parameter space, using 206 URL 201 using 200 ensembles clustering 230 evidence accumulation 230-233 implementing 235, 236 working 234 environment setting up 261, 262 epochs 174 Euclidean distance 27 evaluation, of clustering algorithms references 305 Evidence Accumulation Clustering (EAC) about 230 defining 230 Excel, pandas URL 300 F f1-score about 129 computing 130 using 130 feature-based normalization 37 feature creation about 93-95 Principal Component Analysis (PCA) 96, 97 [ 311 ]
feature extraction about 82 common feature patterns 84-86 good features, creating 87 reality, representing in models 82, 83 features, dataset URL 299 feature selection about 88-90 best individual features, selecting 90-93 feed-forward neural network 163 filename, data Age 280 Blogger ID 280 Gender 280 Industry 280 Star Sign 280 FP-growth algorithm 63 frequent itemsets 63 functions, transformer fit() 99 transform() 99 function words about 192, 193 classifying with 195 counting 193, 194 G GPU avenues, defining 259 benefits 258 code, running on 259, 260 using, for computation 258, 259 GPU optimization 258 graph creating 145-147 gzip 201 H Hadoop about 278 courses 307 Hadoop Distributed File System (HDFS) 279 HBase 279 Hive 279 Pig 279 YARN 279 Hadoop MapReduce 279 hash function 219 hidden layer about 164 creating 251 hierarchical clustering 230 I image extracting 243 image datasets URL 307 input layer 163 installation instructions, scikit-learn URL 7 instructions, AWS CLI URL 294 intra-cluster distance 155 Ionosphere about 29 URL 29 Ionosphere Nearest Neighbor 30 IPython installing 5, 6 URL 5 IPython Notebook creating 107 URL 298 using 3 J Jaccard Similarity 148 JQuery library 113 JSON about 109 and dataset, comparing 109 [ 312 ]
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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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Table of Contents Chapter 7: Discov
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Table of Contents GPU optimization
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Preface If you have ever wanted to
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What you need for this book It shou
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Preface Reader feedback Feedback fr
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Getting Started with Data Mining We
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Chapter 1 In the preceding dataset,
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After you have the above "Hello, wo
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Chapter 1 Windows users may need to
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Chapter 1 The dataset we are going
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Chapter 1 As an example, we will co
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We get the names of the features fo
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Chapter 1 Two rules are near the to
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Chapter 1 The scikit-learn library
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We then iterate over all the sample
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Chapter 1 Overfitting is the proble
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Chapter 1 Summary In this chapter,
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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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Page 295 and 296: Working with Big Data Big data What
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Page 313 and 314: Working with Big Data Next, we crea
Page 315 and 316: Working with Big Data Then, make a
Page 317 and 318: Working with Big Data Left-click th
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Page 321 and 322: Next Steps… Extending the IPython
Page 323 and 324: Next Steps… Chapter 3: Predicting
Page 325 and 326: Next Steps… Vowpal Wabbit http://
Page 327 and 328: Next Steps… Deeper networks These
Page 329 and 330: Next Steps… Real-time clusterings
Page 331 and 332: Next Steps… More resources Kaggle
Page 333: authorship, attributing 185-188 AWS
Page 337 and 338: NetworkX about 145 defining 303 URL
Page 339 and 340: scikit-learn package references 305
Page 342 and 343: Thank you for buying Learning Data
Page 344: Learning Python Data Visualization
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