Learning Data Mining with Python: Use Python to manipulate data and build predictive models , Second Edition

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Classifying with scikit-learn Estimators

The scikit-learn library is a collection of data mining algorithms, written in Python and using a. This library allows users to easily try different algorithms as well as utilize standard tools for doing effective testing and parameter searching. There are many algorithms and utilities in scikit-learn, including many of the commonly used algorithms in modern machine learning.

In this chapter, we focus on setting up a good framework for running data mining procedures. We will use this framework in later chapters, which focus on applications and techniques to use in those situations.

The key concepts introduced in this chapter are as follows:

Estimators: This is to perform classification, clustering, and regression
Transformers: This is to perform pre-processing and data alterations
Pipelines: This is to put together your workflow into a replicable format

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Key benefits

Use a wide variety of Python libraries for practical data mining purposes.

Learn how to find, manipulate, analyze, and visualize data using Python.

Step-by-step instructions on data mining techniques with Python that have real-world applications.

Description

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations.

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

Perform object detection in images using Deep Neural Networks

Find meaningful insights from your data through intuitive visualizations

Compute on big data, including real-time data from the internet

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