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Chapter 4 We will sample our dataset to form a training dataset. This also helps reduce the size of the dataset that will be searched, making the Apriori algorithm run faster. We obtain all reviews from the first 200 users: ratings = all_ratings[all_ratings['UserID'].isin(range(200))] Next, we can create a dataset of only the favorable reviews in our sample: favorable_ratings = ratings[ratings["Favorable"]] We will be searching the user's favorable reviews for our itemsets. So, the next thing we need is the movies which each user has given a favorable. We can <strong>com</strong>pute this by grouping the dataset by the User ID and iterating over the movies in each group: favorable_reviews_by_users = dict((k, frozenset(v.values)) for k, v in favorable_ratings groupby("UserID")["MovieID"]) In the preceding code, we stored the values as a frozenset, allowing us to quickly check if a movie has been rated by a user. Sets are much faster than lists for this type of operation, and we will use them in a later code. Finally, we can create a DataFrame that tells us how frequently each movie has been given a favorable review: num_favorable_by_movie = ratings[["MovieID", "Favorable"]]. groupby("MovieID").sum() We can see the top five movies by running the following code: num_favorable_by_movie.sort("Favorable", ascending=False)[:5] Let's see the top five movies list: MovieID Favorable 50 100 100 89 258 83 181 79 174 74 [ 67 ]
Re<strong>com</strong>mending Movies Using Affinity Analysis The Apriori algorithm The Apriori algorithm is part of our affinity analysis and deals specifically with finding frequent itemsets within the data. The basic procedure of Apriori builds up new candidate itemsets from previously discovered frequent itemsets. These candidates are tested to see if they are frequent, and then the algorithm iterates as explained here: 1. Create initial frequent itemsets by placing each item in its own itemset. Only items with at least the minimum support are used in this step. 2. New candidate itemsets are created from the most recently discovered frequent itemsets by finding supersets of the existing frequent itemsets. 3. All candidate itemsets are tested to see if they are frequent. If a candidate is not frequent then it is discarded. If there are no new frequent itemsets from this step, go to the last step. 4. Store the newly discovered frequent itemsets and go to the second step. 5. Return all of the discovered frequent itemsets. This process is outlined in the following workflow: [ 68 ]
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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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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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Page 44 and 45: Chapter 1 Overfitting is the proble
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Page 86 and 87: Chapter 4 The classic algorithm for
Page 88 and 89: Chapter 4 When loading the file, we
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Page 94 and 95: Chapter 4 We want to break out the
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Page 104 and 105: Extracting Features with Transforme
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Page 108 and 109: Chapter 5 Other features describe a
Page 110 and 111: Chapter 5 Similarly, we can convert
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Page 120 and 121: Chapter 5 The downside to transform
Page 122 and 123: Chapter 5 A transformer is akin to
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Page 126: 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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