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Python Data Mining Quick Start Guide

By : Greeneltch

5 (10)

Buy this Book

Python Data Mining Quick Start Guide

5 (10)

By: Greeneltch

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Data Mining and Getting Started with Python Tools

Descriptive, predictive, and prescriptive analytics

What will and will not be covered in this book

Setting up Python environments for data mining

Installing the Anaconda distribution and Conda package manager

Launching the Spyder IDE

Launching a Jupyter Notebook

Installing high-performance Python distribution

Recommended libraries and how to install

Summary

Basic Terminology and Our End-to-End Example

Basic data terminology

Basic summary statistics

An end-to-end example of data mining in Python

Summary

Collecting, Exploring, and Visualizing Data

Types of data sources and loading into pandas

Access, search, and sanity checks with pandas

Basic plotting in Seaborn

Popular types of plots for visualizing data

Summary

Cleaning and Readying Data for Analysis

The scikit-learn transformer API

Cleaning input data

High-dimensional data

Dimension reduction

Summary

Grouping and Clustering Data

Introducing clustering concepts

Clustering methods

Summary

Prediction with Regression and Classification

Scikit-learn Estimator API

Introducing prediction concepts

Regression

Classification

Tuning a prediction model

Summary

Advanced Topics - Building a Data Processing Pipeline and Deploying It

Pipelining your analysis

Deploying the model

Python-specific deployment concerns

Summary

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Collecting, Exploring, and Visualizing Data

As a first step, it is important to understand how to acquire data and how to access it in computer memory. Once data is loaded, sound practices for exploration can save time downstream. This chapter will start by showing you how to interact with different data sources such as databases, disks, and streaming. Despite the practicality of these topics, many new analysts overlook them. Indeed, you cannot actually do any work if you cannot get past this beginning step. The second half of the chapter will introduce you to Seaborn for visualizing data, and then recommend types of plots for relevant and popular problem statements.

The following topics will be covered in this chapter: