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Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models /
If you are a programmer who wants to get started with data mining, then this book is for you.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
|
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Layton, Robert, |e author. | |
| 245 | 1 | 0 | |a Learning data mining with Python : |b harness the power of Python to analyze data and create insightful predictive models / |c Robert Layton. |
| 246 | 3 | 0 | |a Harness the power of Python to analyze data and create insightful predictive models |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2015. | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource (xiv, 317 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file | ||
| 490 | 1 | |a Community experience distilled | |
| 500 | |a Includes index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Getting Started with Data Mining -- Introducing data mining -- Using Python and the IPython notebook -- Installing Python -- Installing IPython -- Installing scikit-learn -- A simple affinity analysis example -- What is affinity analysis? -- Product recommendations -- Loading the dataset with NumPy -- Implementing a simple ranking of rules -- Ranking to find the best rules -- A simple classification example | |
| 505 | 8 | |a What is classification?Loading and preparing the dataset -- Implementing the OneR algorithm -- Testing the algorithm -- Summary -- Chapter 2: Classifying with scikit-learn -- scikit-learn estimators -- Nearest neighbors -- Distance metrics -- Loading the dataset -- Moving towards a standard workflow -- Running the algorithm -- Setting parameters -- Preprocessing using pipelines -- An example -- Standard preprocessing -- Putting it all together -- Pipelines -- Summary -- Chapter 3: Predicting Sports Winners with Decision Trees | |
| 505 | 8 | |a Loading the datasetCollecting the data -- Using pandas to load the dataset -- Cleaning up the dataset -- Extracting new features -- Decision trees -- Parameters in decision trees -- Using decision trees -- Sports outcome prediction -- Putting it all together -- Random forests -- How do ensembles work? -- Parameters in Random forests -- Applying Random forests -- Engineering new features -- Summary -- Chapter 4: Recommending Movies Using Affinity Analysis -- Affinity analysis -- Algorithms for affinity analysis -- Choosing parameters | |
| 505 | 8 | |a The movie recommendation problemObtaining the dataset -- Loading with pandas -- Sparse data formats -- The Apriori implementation -- The Apriori algorithm -- Implementation -- Extracting association rules -- Evaluation -- Summary -- Chapter 5: Extracting Features with Transformers -- Feature extraction -- Representing reality in models -- Common feature patterns -- Creating good features -- Feature selection -- Selecting the best individual features -- Feature creation -- Principal Component Analysis -- Creating your own transformer | |
| 505 | 8 | |a The transformer APIImplementation details -- Unit testing -- Putting it all together -- Summary -- Chapter 6: Social Media Insight Using Naive Bayes -- Disambiguation -- Downloading data from a social network -- Loading and classifying the dataset -- Creating a replicable dataset from Twitter -- Text transformers -- Bag-of-words -- N-grams -- Other features -- Naive Bayes -- Bayes' theorem -- Naive Bayes algorithm -- How it works -- Application -- Extracting word counts -- Converting dictionaries to a matrix | |
| 520 | |a If you are a programmer who wants to get started with data mining, then this book is for you. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Data mining. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 7 | |a COMPUTERS |x Programming Languages |x Python. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Databases |x Data Mining. |2 bisacsh | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Layton, Robert. |t Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models. |d Birmingham, England ; Mumbai, [India] : Packt Publishing, ©2015 |h xiv, 317 pages |z 9781784396053 |
| 830 | 0 | |a Community experience distilled. | |
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