Harness the power of Python to analyze data and create insightful predictive models
About This Book
- Learn data mining in practical terms, using a wide variety of libraries and techniques
- Learn how to find, manipulate, and analyze data using Python
- Step-by-step instructions on creating real-world applications of data mining techniques
Who This Book Is For
If you are a programmer who wants to get started with data mining, then this book is for you.
What You Will Learn
- Apply data mining concepts to real-world problems
- Predict the outcome of sports matches based on past results
- Determine the author of a document based on their writing style
- Use APIs to download datasets from social media and other online services
- Find and extract good features from difficult datasets
- Create models that solve real-world problems
- Design and develop data mining applications using a variety of datasets
- Set up reproducible experiments and generate robust results
- Recommend movies, online celebrities, and news articles based on personal preferences
- Compute on big data, including real-time data from the Internet
In Detail
The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis.
This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems.
There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK.
Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.
Robert Layton
Robert Layton has a PhD in computer science and has been an avid Python programmer for many years. He has worked closely with some of the largest companies in the world on data mining applications for real-world data and has also been published extensively in international journals and conferences. He has extensive experience in cybercrime and text-based data analytics, with a focus on behavioral modeling, authorship analysis, and automated open source intelligence. He has contributed code to a number of open source libraries, including the scikit-learn library used in this book, and was a Google Summer of Code mentor in 2014. Robert runs a data mining consultancy company called dataPipeline, providing data mining and analytics solutions to businesses in a variety of industries.
