Python Data Mining Quick Start Guide

Book Description

Explore different data mining techniques using Python libraries and packages

Key Features

• Grasp the basics of data loading, cleaning, analysis, and visualization
• Use popular Python libraries such as NumPy, pandas, Matplotlib, and scikit-learn for data mining
• Your one-stop guide to building efficient data mining pipelines without going into too much theory

Book Description

Data mining involves the use of tools and techniques to identify unique and useful patterns in a dataset. Thanks to its rich ecosystem of libraries used for data analysis, manipulation, and machine learning, Python has emerged as a popular tool for performing data mining. This book is a quick primer on how to get started with using Python for effective data mining.

Starting with a quick introduction to the concept of data mining, this book will help you put it to practical use with the help of popular Python packages and libraries. You’ll get a demonstration of working with different real-world datasets and extracting insights from them Python libraries such as NumPy, pandas, scikit-learn, and Matplotlib. The book will then learn take you through the different stages of data mining—loading, cleaning, analysis, and data visualization. You’ll also explore widely used data transformation, clustering, and classification techniques.

By the end of this book, you’ll be able to build an efficient data mining pipeline using Python with ease.

What you will learn

• Explore methods for summarizing datasets and visualizing/plotting data
• Collect and format data for analytical work
• Assign data points into groups and visualize clustering patterns
• Predict continuous and categorical output for your data
• Clean, filter noise from, and reduce the dimensions of data
• Serialize a data processing model using scikit-learn’s pipeline feature
• Deploy your data processing model using Python’s pickle module

Who this book is for

If you’re a Python developer interested in getting started with data mining, this book is for you. Budding data scientists and data analysts with Python programming knowledge will also find this book useful for getting up to speed with practical data mining using Python.

Table of Contents

1. Data Mining and Getting Started with Python Tools
2. Basic Terminology and Our End-to-End Example
3. Collecting, Exploring, and Visualizing Data
4. Cleaning and Readying Data for Analysis
5. Grouping and Clustering Data
6. Prediction with Regression and Classification
7. Advanced Topics – Building a Data Processing Pipeline and Deploying It

中文：

书名：Python数据挖掘快速入门指南

使用Python库和包探索不同的数据挖掘技术

主要特点

• 掌握数据加载、清理、分析和可视化的基础知识
• 使用流行的Python库，如NumPy、Pandas、Matplotlib和SCRICKIT-学习数据挖掘
• 您的一站式指南，无需太多理论即可构建高效的数据挖掘管道

Book Description

数据挖掘涉及使用工具和技术来识别数据集中唯一和有用的模式。由于其用于数据分析、操作和机器学习的丰富的库生态系统，Python已成为执行数据挖掘的流行工具。这本书是一本快速入门的入门读物，介绍了如何使用Python进行有效的数据挖掘。

从快速介绍数据挖掘的概念开始，本书将帮助您在流行的Python包和库的帮助下将其付诸实践。您将看到如何使用不同的真实数据集并从中提取见解的演示。然后，本书将带您经历数据挖掘的不同阶段–加载、清理、分析和数据可视化。您还将探索广泛使用的数据转换、集群和分类技术。

到本书结束时，您将能够轻松地使用Python构建高效的数据挖掘管道。

What you will learn

• 探索汇总数据集和可视化/绘制数据的方法
• 为分析工作收集和格式化数据
• 将数据点分配到组中并可视化聚类模式
• Predict continuous and categorical output for your data
• 对数据进行清理、过滤噪声并降低维度
• 使用SCRICKIT-LINE的管道特性序列化数据处理模型
• 使用Python的Pickle模块部署数据处理模型

Who this book is for

如果您是一名对数据挖掘入门感兴趣的Python开发人员，这本书是为您准备的。初出茅庐的数据科学家和具有Python编程知识的数据分析师也会发现，这本书对快速掌握使用Python进行实际数据挖掘很有帮助。

Table of Contents

1. 数据挖掘和Python工具入门
2. 基本术语和我们的端到端示例
3. 收集、浏览和可视化数据
4. Cleaning and Readying Data for Analysis
5. Grouping and Clustering Data
6. 回归与分类预测
7. 高级主题&构建和部署数据处理管道