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.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2015.
|
| Schriftenreihe: | Community experience distilled.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | If you are a programmer who wants to get started with data mining, then this book is for you. |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource (xiv, 317 pages) |
| ISBN: | 9781784391201 1784391204 |
Internformat
MARC
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| 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 | |
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| 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 |
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| contents | 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 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 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 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 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 |
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| discipline | Informatik |
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| series2 | Community experience distilled |
| spelling | Layton, Robert, author. Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models / Robert Layton. Harness the power of Python to analyze data and create insightful predictive models Birmingham, UK : Packt Publishing, 2015. ©2015 1 online resource (xiv, 317 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Community experience distilled Includes index. Print version record. 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 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 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 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 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 If you are a programmer who wants to get started with data mining, then this book is for you. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Python (Langage de programmation) Exploration de données (Informatique) COMPUTERS Programming Languages Python. bisacsh COMPUTERS Databases Data Mining. bisacsh Data mining fast Python (Computer program language) fast has work: Learning data mining with Python (Text) https://id.oclc.org/worldcat/entity/E39PCYyJbc8PfPfrQcF43VWfJC https://id.oclc.org/worldcat/ontology/hasWork Print version: Layton, Robert. Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models. Birmingham, England ; Mumbai, [India] : Packt Publishing, ©2015 xiv, 317 pages 9781784396053 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1046540 Volltext |
| spellingShingle | Layton, Robert Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models / Community experience distilled. 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 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 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 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 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 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Python (Langage de programmation) Exploration de données (Informatique) COMPUTERS Programming Languages Python. bisacsh COMPUTERS Databases Data Mining. bisacsh Data mining fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh97002073 |
| title | Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models / |
| title_alt | Harness the power of Python to analyze data and create insightful predictive models |
| title_auth | Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models / |
| title_exact_search | Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models / |
| title_full | Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models / Robert Layton. |
| title_fullStr | Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models / Robert Layton. |
| title_full_unstemmed | Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models / Robert Layton. |
| title_short | Learning data mining with Python : |
| title_sort | learning data mining with python harness the power of python to analyze data and create insightful predictive models |
| title_sub | harness the power of Python to analyze data and create insightful predictive models / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Python (Langage de programmation) Exploration de données (Informatique) COMPUTERS Programming Languages Python. bisacsh COMPUTERS Databases Data Mining. bisacsh Data mining fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Data mining. Python (Langage de programmation) Exploration de données (Informatique) COMPUTERS Programming Languages Python. COMPUTERS Databases Data Mining. Data mining |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1046540 |
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