Learning Data Mining with Python

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Chapter 3 If you are facing trouble extracting features of these types, check the pandas documentation at http://pandas.pydata.org/pandas-docs/stable/ for help. Alternatively, you can try an online forum such as Stack Overflow for assistance. More extreme examples could use player data to estimate the strength of each team's sides to predict who won. These types of complex features are used every day by gamblers and sports betting agencies to try to turn a profit by predicting the outcome of sports matches. Summary In this chapter, we extended our use of scikit-learn's classifiers to perform classification and introduced the pandas library to manage our data. We analyzed real-world data on basketball results from the NBA, saw some of the problems that even well-curated data introduces, and created new features for our analysis. We saw the effect that good features have on performance and used an ensemble algorithm, Random forests, to further improve the accuracy. In the next chapter, we will extend the affinity analysis that we performed in the first chapter to create a program to find similar books. We will see how to use algorithms for ranking and also use approximation to improve the scalability of data mining. [ 59 ]
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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
Page 32 and 33: Chapter 1 The dataset we are going
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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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Page 81: Predicting Sports Winners with Deci
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Page 105 and 106: Extracting Features with Transforme
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In the preceding loop, we also perf
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Chapter 6 Next, we create a simple
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Chapter 6 For this cell, we will be
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Chapter 6 On running the preceding
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Chapter 6 The code is as follows: a
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Chapter 6 Here's an excerpt from Th
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As an example, for n=3, we extract
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Chapter 6 From here, we use Bayes'
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Now, we can compute the probability
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Chapter 6 Let's take a look at the
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We can nearly run our pipeline now,
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Chapter 6 Note that we aren't reall
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Chapter 6 Summary In this chapter,
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Discovering Accounts to Follow Usin
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Beating CAPTCHAs with Neural Networ
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Authorship Attribution Authorship a
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Authorship studies alone cannot pro
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Getting the data The data we will u
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Chapter 9 We create lists for stori
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Chapter 9 The use of function words
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Classifying with function words Nex
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Chapter 9 The derivation of these e
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Chapter 9 Character n-grams are fou
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Chapter 9 Accessing the Enron datas
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Next, we iterate through each of th
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Chapter 9 This document contains an
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Chapter 9 Evaluation It is generall
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Chapter 9 We can see that authors a
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Clustering News Articles Our system
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Clustering News Articles Now let's
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Clustering News Articles The URL fo
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Clustering News Articles As the las
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Clustering News Articles If there i
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Clustering News Articles At this po
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Clustering News Articles The algori
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Clustering News Articles The labels
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Clustering News Articles After this
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Clustering News Articles You can th
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Clustering News Articles In graph t
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Clustering News Articles How it wor
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Clustering News Articles We then wr
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Clustering News Articles We can the
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Clustering News Articles Summary In
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Classifying Objects in Images Using
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Working with Big Data The amount of
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Chapter 12 In big data, we can't lo
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Chapter 12 MapReduce originates fro
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Chapter 12 The map function takes a
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The Hadoop ecosystem is quite compl
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Chapter 12 We set a test filename s
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Chapter 12 Extracting the blog post
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Chapter 12 The first parameter, /bl
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Chapter 12 The first function is th
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We again redefine our word search r
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Chapter 12 One problem with using l
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Chapter 12 for line in inf: tokens
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Chapter 12 python extract_posts.py
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Next Steps… During the course of
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Appendix To install it, clone the r
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Chapter 4 - Recommending Movies Usi
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Chapter 7 - Discovering Accounts to
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Local n-grams https://github.com/ro
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Appendix Other image datasets are a
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Index A access keys 107 accuracy im
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example 2 features 2 follower infor
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K Kaggle about 308 URL 308 Keras UR
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preprocessing, using pipelines abou
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U UCL Machine Learning data reposit
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Python Data Analysis ISBN: 978-1-78
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Learning Data Mining with Python

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