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Python Data Mining Quick Start Guide

Python Data Mining Quick Start Guide

By : Greeneltch
5 (10)
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Python Data Mining Quick Start Guide

Python Data Mining Quick Start Guide

5 (10)
By: Greeneltch

Overview of this book

Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle.
Table of Contents (9 chapters)
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Advanced Topics - Building a Data Processing Pipeline and Deploying It

This chapter will cover the strategy of building a data analysis pipeline and deploying it to run in production on future, incoming data. It will also cover persistent model storage, which is required to distribute for deployment. This chapter will then cover the specific consequences that Python's interpreted nature has on deployment.

The following topics will be covered in this chapter:

  • Pipelining your analysis
  • Storing a model for deployment
  • Loading a deployed model
  • Python-specific deployment concerns

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