Learning data mining with Python
Subtitle: harness the power of Python to analyze data and create insightful predictive models
By: Robert Layton
Series: Community experience distilled; Community experience distilled
Publisher: Packt Publishing
Published: 2015
Place of publication: Birmingham, UK
Language: eng
Number of pages: 317
ISBN-13: 9781784391201; 9781784396053
Copyright date: 2015
Subjects: Python (Computer program language); Data mining; COMPUTERS; Programming Languages; Python; Databases; Data Mining
Other titles: Harness the power of Python to analyze data and create insightful predictive models
Description: If you are a programmer who wants to get started with data mining, then this book is for you.
Notes: Includes index.
Table of contents: title: Cover; title: Copyright; title: Credits; title: About the Author; title: About the Reviewers; title: www.PacktPub.com; title: Table of Contents; title: Preface; title: Chapter 1: Getting Started with Data Mining; title: Introducing data mining; title: Using Python and the IPython notebook; title: Installing Python; title: Installing IPython; title: Installing scikit-learn; title: A simple affinity analysis example; title: What is affinity analysis?; title: Product recommendations; title: Loading the dataset with NumPy; title: Implementing a simple ranking of rules; title: Ranking to find the best rules; title: A simple classification example; title: What is classification?Loading and preparing the dataset; title: Implementing the OneR algorithm; title: Testing the algorithm; title: Summary; title: Chapter 2: Classifying with scikit-learn; title: scikit-learn estimators; title: Nearest neighbors; title: Distance metrics; title: Loading the dataset; title: Moving towards a standard workflow; title: Running the algorithm; title: Setting parameters; title: Preprocessing using pipelines; title: An example; title: Standard preprocessing; title: Putting it all together; title: Pipelines; title: Summary; title: Chapter 3: Predicting Sports Winners with Decision Trees; title: Loading the datasetCollecting the data; title: Using pandas to load the dataset; title: Cleaning up the dataset; title: Extracting new features; title: Decision trees; title: Parameters in decision trees; title: Using decision trees; title: Sports outcome prediction; title: Putting it all together; title: Random forests; title: How do ensembles work?; title: Parameters in Random forests; title: Applying Random forests; title: Engineering new features; title: Summary; title: Chapter 4: Recommending Movies Using Affinity Analysis; title: Affinity analysis; title: Algorithms for affinity analysis; title: Choosing parameters; title: The movie recommendation problemObtaining the dataset; title: Loading with pandas; title: Sparse data formats; title: The Apriori implementation; title: The Apriori algorithm; title: Implementation; title: Extracting association rules; title: Evaluation; title: Summary; title: Chapter 5: Extracting Features with Transformers; title: Feature extraction; title: Representing reality in models; title: Common feature patterns; title: Creating good features; title: Feature selection; title: Selecting the best individual features; title: Feature creation; title: Principal Component Analysis; title: Creating your own transformer; title: The transformer APIImplementation details; title: Unit testing; title: Putting it all together; title: Summary; title: Chapter 6: Social Media Insight Using Naive Bayes; title: Disambiguation; title: Downloading data from a social network; title: Loading and classifying the dataset; title: Creating a replicable dataset from Twitter; title: Text transformers; title: Bag-of-words; title: N-grams; title: Other features; title: Naive Bayes; title: Bayes' theorem; title: Naive Bayes algorithm; title: How it works; title: Application; title: Extracting word counts; title: Converting dictionaries to a matrix