Learning data mining with Python: use Python to manipulate data and build predictive models
Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting Started with Data Mining -- Introducing data mining -- Using Python and the Jupyter Notebook -- Installing Python -- Installing Jupyt...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham
Packt Publishing
2017
|
| Edition: | Second edition |
| Subjects: | |
| Online Access: | BTW01 Volltext |
| Summary: | Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting Started with Data Mining -- Introducing data mining -- Using Python and the Jupyter Notebook -- Installing Python -- Installing Jupyter Notebook -- Installing scikit-learn -- A simple affinity analysis example -- What is affinity analysis? -- Product recommendations -- Loading the dataset with NumPy -- Downloading the example code -- 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 Estimators -- scikit-learn estimators -- Nearest neighbors -- Distance metrics -- Loading the dataset -- Moving towards a standard workflow -- Running the algorithm -- Setting parameters -- Preprocessing -- Standard pre-processing -- Putting it all together -- Pipelines -- Summary -- Chapter 3: Predicting Sports Winners with Decision Trees -- Loading the dataset -- Collecting 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? -- Setting parameters in Random Forests -- Applying random forests -- Engineering new features -- Summary -- Chapter 4: Recommending Movies Using Affinity Analysis -- Affinity analysis -- Algorithms for affinity analysis -- Overall methodology -- Dealing with the movie recommendation problem -- Obtaining the dataset -- Loading with pandas -- Sparse data formats -- Understanding the Apriori algorithm and its implementation. |
| Physical Description: | 1 online resource (348 pages) |
| ISBN: | 9781787129566 |
Staff View
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| spelling | Layton, Robert Verfasser aut Learning data mining with Python use Python to manipulate data and build predictive models Robert Layton Second edition Birmingham Packt Publishing 2017 1 online resource (348 pages) txt rdacontent c rdamedia cr rdacarrier Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting Started with Data Mining -- Introducing data mining -- Using Python and the Jupyter Notebook -- Installing Python -- Installing Jupyter Notebook -- Installing scikit-learn -- A simple affinity analysis example -- What is affinity analysis? -- Product recommendations -- Loading the dataset with NumPy -- Downloading the example code -- 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 Estimators -- scikit-learn estimators -- Nearest neighbors -- Distance metrics -- Loading the dataset -- Moving towards a standard workflow -- Running the algorithm -- Setting parameters -- Preprocessing -- Standard pre-processing -- Putting it all together -- Pipelines -- Summary -- Chapter 3: Predicting Sports Winners with Decision Trees -- Loading the dataset -- Collecting 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? -- Setting parameters in Random Forests -- Applying random forests -- Engineering new features -- Summary -- Chapter 4: Recommending Movies Using Affinity Analysis -- Affinity analysis -- Algorithms for affinity analysis -- Overall methodology -- Dealing with the movie recommendation problem -- Obtaining the dataset -- Loading with pandas -- Sparse data formats -- Understanding the Apriori algorithm and its implementation. Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Python (Computer program language) Electronic books Python Programmiersprache (DE-588)4434275-5 s DE-604 X:EBC https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=4851656 Verlag Volltext |
| spellingShingle | Layton, Robert Learning data mining with Python use Python to manipulate data and build predictive models Python Programmiersprache (DE-588)4434275-5 gnd |
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| title | Learning data mining with Python use Python to manipulate data and build predictive models |
| title_auth | Learning data mining with Python use Python to manipulate data and build predictive models |
| title_exact_search | Learning data mining with Python use Python to manipulate data and build predictive models |
| title_exact_search_txtP | Learning data mining with Python use Python to manipulate data and build predictive models |
| title_full | Learning data mining with Python use Python to manipulate data and build predictive models Robert Layton |
| title_fullStr | Learning data mining with Python use Python to manipulate data and build predictive models Robert Layton |
| title_full_unstemmed | Learning data mining with Python use Python to manipulate data and build predictive models Robert Layton |
| title_short | Learning data mining with Python |
| title_sort | learning data mining with python use python to manipulate data and build predictive models |
| title_sub | use Python to manipulate data and build predictive models |
| topic | Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Python Programmiersprache |
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