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数据准备和特征工程:数据工程师必知必会技能

作者：齐伟

出版社：电子工业出版社出版时间：2020-03-01

开本： 26cm 页数： 198页

本类榜单：计算机/网络销量榜

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版权信息
本书特色
内容简介
目录
作者简介

数据准备和特征工程:数据工程师必知必会技能版权信息

ISBN：9787121382635
条形码：9787121382635 ; 978-7-121-38263-5
装帧：平装-胶订
册数：暂无
重量：暂无
所属分类：
计算机/网络
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数据库
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数据仓库与数据挖掘

数据准备和特征工程:数据工程师必知必会技能本书特色

画龙点睛的批注，让学习更加简单案例式教学，面向工程实践渗透技术发展，基础与前沿结合提供在线实验平台，学练融合在专属公众号与作者交互，教学相长配套视频和在线课程，边看边学

数据准备和特征工程:数据工程师必知必会技能内容简介

本书详细地介绍了大数据、人工智能等项目中不可或缺的环节和内容：数据准备和特征工程。书中的每节首先以简明方式介绍了基本知识；然后通过实际案例演示了基本知识的实际应用，并提供了针对性练习项目，将“知识、案例、练习”融为一体；*后以“扩展探究”方式引导读者进入更深广的领域。本书既适合作为大学相关专业的教材，也适合作为大数据、人工智能等领域的开发人员的参考读物。

数据准备和特征工程:数据工程师必知必会技能目录

目录第1 章感知数据 ·································.001 1.0 了解数据科学项目 ································001 1.1 文件中的数据 ··································003 1.1.1 CSV文件 ····································003 1.1.2 Excel文件 ···································009 1.1.3 图像文件 ···································015 1.2 数据库中的数据 ·································019 1.3 网页上的数据 ··································029 1.4 来自API 的数据 ·································039 第2 章数据清理 ··································044 2.0 基本概念 ····································045 2.1 转化数据类型 ··································046 2.2 处理重复数据 ··································054 2.3 处理缺失数据 ··································057 2.3.1 检查缺失数据 ·································058 2.3.2 用指定值填补 ·································063 2.3.3 根据规律填补 ·································069 2.4 处理离群数据 ··································076 第3 章特征变换 ···································083 3.0 特征的类型 ···································084 3.1 特征数值化 ···································085 3.2 特征二值化 ···································088 3.3 OneHot编码 ···································093 3.4 数据变换 ····································098 3.5 特征离散化 ···································104 3.5.1 无监督离散化 ·································104 3.5.2 有监督离散化 ·································110 3.6 数据规范化 ···································113 第4 章特征选择 ···································124 4.0 特征选择简述 ··································124 4.1 封装器法 ····································127 4.1.1 循序特征选择 ·································127 4.1.2 穷举特征选择 ·································135 4.1.3 递归特征消除 ·································140 4.2 过滤器法 ····································144 4.3 嵌入法 ·····································149 第5 章特征抽取 ···································154 5.1 无监督特征抽取··································154 5.1.1 主成分分析 ··································154 5.1.2 因子分析 ···································161 5.2 有监督特征抽取 ·································167 附录A Jupyter简介 ·································173 附录B NumPy简介 ··································176 附录C Pandas简介 ··································185 附录D Matplotlib简介 ································194 后记 ········································199

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