没有合适的资源?快使用搜索试试~ 我知道了~
首页Python数据挖掘实战:构建预测模型与应用
"《Learning Data Mining with Python》是一本实用的指南,作者是Robert Layton,专为希望利用Python进行数据挖掘的程序员设计。本书旨在通过实际操作,教授读者如何运用多种Python库和技术来理解和分析数据,以及创建具有洞察力的预测模型,以解决现实世界的问题。 书中的内容覆盖了数据挖掘的基本概念,如分类和亲和性分析,随后深入探讨了更复杂的类型,如文本、图像和图数据。每个章节都包含具体的实例,如预测体育比赛结果、根据写作风格识别文档作者、使用API从社交媒体获取数据等。书中特别强调了使用诸如IPython Notebook、pandas、scikit-learn和NLTK等Python库的重要性,这些库在数据挖掘领域提供了强大的功能和灵活性。 读者将学习如何应用各种算法和技术,包括决策树、协同过滤、神经网络、文本特征提取和深度学习等。此外,书中还介绍了如何处理大数据,包括实时互联网数据,以及如何设计和开发数据挖掘应用程序,确保实验可重复性和结果的稳健性。最后,通过推荐电影、网络名人和新闻文章等应用场景,帮助读者理解数据挖掘在现实生活中的实际应用。 《Learning Data Mining with Python》是一本全面且深入的数据挖掘教程,适合希望通过Python提升数据分析技能的编程人员。无论你是初学者还是有一定经验的开发者,都能从中学到丰富的知识,从而更好地挖掘和利用数据中的价值。"
资源详情
资源推荐
![](https://csdnimg.cn/release/download_crawler_static/9029399/bg10.jpg)
Table of Contents
[ vii ]
Chapter 5 – Extracting Features with Transformers 301
Adding noise 301
Vowpal Wabbit 302
Chapter 6 – Social Media Insight Using Naive Bayes 302
Spam detection 302
Natural language processing and part-of-speech tagging 302
Chapter 7 – Discovering Accounts to Follow Using Graph Mining 303
More complex algorithms 303
NetworkX 303
Chapter 8 – Beating CAPTCHAs with Neural Networks 303
Better (worse?) CAPTCHAs 303
Deeper networks 304
Reinforcement learning 304
Chapter 9 – Authorship Attribution 304
Increasing the sample size 304
Blogs dataset 304
Local n-grams 305
Chapter 10 – Clustering News Articles 305
Evaluation 305
Temporal analysis 305
Real-time clusterings 306
Chapter 11: Classifying Objects in Images Using Deep Learning 306
Keras and Pylearn2 306
Mahotas 306
Chapter 12 – Working with Big Data 307
Courses on Hadoop 307
Pydoop 307
Recommendation engine 307
More resources 308
Index 309
![](https://csdnimg.cn/release/download_crawler_static/9029399/bg11.jpg)
![](https://csdnimg.cn/release/download_crawler_static/9029399/bg12.jpg)
[ ix ]
Preface
If you have ever wanted to get into data mining, but didn't know where to start,
I've written this book with you in mind.
Many data mining books are highly mathematical, which is great when you
are coming from such a background, but I feel they often miss the forest for the
trees—that is, they focus so much on how the algorithms work, that we forget
about why we are using these algorithms.
In this book, my aim has been to create a book for those who can program and
want to learn data mining. By the end of this book, my aim is that you have a good
understanding of the basics, some best practices to jump into solving problems with
data mining, and some pointers on the next steps you can take.
Each chapter in this book introduces a new topic, algorithm, and dataset. For this
reason, it can be a bit of a whirlwind tour, moving quickly from topic to topic.
However, for each of the chapters, think about how you can improve upon the
results presented in the chapter. Then, take a shot at implementing it!
One of my favorite quotes is from Shakespeare's Henry IV:
But will they come when you do call for them?
Before this quote, a character is claiming to be able to call spirits. In response,
Hotspur points out that anyone can call spirits, but what matters is whether they
actually come when they are called.
In much the same way, learning data mining is about performing experiments and
getting the result. Anyone can come up with an idea to create a new data mining
algorithm or improve upon an experiment's results. However, what matters is: can
you build it and does it work?
![](https://csdnimg.cn/release/download_crawler_static/9029399/bg13.jpg)
Preface
[ x ]
What this book covers
Chapter 1, Getting Started with Data Mining, introduces the technologies we will be
using, along with implementing two basic algorithms to get started.
Chapter 2, Classifying with scikit-learn Estimators, covers classication, which is a key
form of data mining. You'll also learn about some structures to make your data
mining experimentation easier to perform..
Chapter 3, Predicting Sports Winners with Decision Trees, introduces two new
algorithms, Decision Trees and Random Forests, and uses them to predict sports
winners by creating useful features.
Chapter 4, Recommending Movies Using Afnity Analysis, looks at the problem
of recommending products based on past experience and introduces the
Apriori algorithm.
Chapter 5, Extracting Features with Transformers, introduces different types of features
you can create and how to work with different datasets.
Chapter 6, Social Media Insight Using Naive Bayes, uses the Naive Bayes algorithm to
automatically parse text-based information from the social media website, Twitter.
Chapter 7, Discovering Accounts to Follow Using Graph Mining, applies cluster and
network analysis to nd good people to follow on social media.
Chapter 8, Beating CAPTCHAs with Neural Networks, looks at extracting
information from images and then training neural networks to nd words
and letters in those images.
Chapter 9, Authorship Attribution, looks at determining who wrote a given document,
by extracting text-based features and using support vector machines.
Chapter 10, Clustering News Articles, uses the k-means clustering algorithm to group
together news articles based on their content.
Chapter 11, Classifying Objects in Images Using Deep Learning, determines what type of
object is being shown in an image, by applying deep neural networks.
Chapter 12, Working with Big Data, looks at workows for applying algorithms to big
data and how to get insight from it.
Appendix, Next Steps…, goes through each chapter, giving hints on where to go
next for a deeper understanding of the concepts introduced.
![](https://csdnimg.cn/release/download_crawler_static/9029399/bg14.jpg)
Preface
[ xi ]
What you need for this book
It should come as no surprise that you'll need a computer, or access to one, to
complete this book. The computer should be reasonably modern, but it doesn't
need to be overpowered. Any modern processor (from about 2010 onwards) and
4 GB of RAM will sufce, and you can probably run almost all of the code on a
slower system too.
The exception here is with the nal two chapters. In these chapters, I step through
using Amazon Web Services (AWS) to run the code. This will probably cost you
some money, but the advantage is less system setup than running the code locally.
If you don't want to pay for those services, the tools used can all be set up on a local
computer, but you will denitely need a modern system to run it. A processor built
in at least 2012 and with more than 4 GB of RAM is necessary.
I recommend the Ubuntu operating system, but the code should work well
on Windows, Macs, or any other Linux variant. You may need to consult the
documentation for your system to get some things installed, though.
In this book, I use pip to install code, which is a command-line tool for installing
Python libraries. Another option is to use Anaconda, which can be found online here:
http://continuum.io/downloads.
I have also tested all code using Python 3. Most of the code examples work on
Python 2, with no changes. If you run into any problems and can't get around them,
send an email and we can offer a solution.
Who this book is for
This book is for programmers who want to get started in data mining in an
application-focused manner.
If you haven't programmed before, I strongly recommend that you learn at least
the basics before you get started. This book doesn't introduce programming, nor
does it give too much time to explain the actual implementation (in code) of how
to type out the instructions. That said, once you go through the basics, you should
be able to come back to this book fairly quickly—there is no need to be an expert
programmer rst!
I highly recommend that you have some Python programming experience. If you
don't, feel free to jump in, but you might want to take a look at some Python code
rst, possibly focusing on tutorials using the IPython Notebook. Writing programs in
the IPython Notebook works a little differently than other methods such as writing a
Java program in a fully edged IDE.
剩余343页未读,继续阅读
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://profile-avatar.csdnimg.cn/e66324aa22894c0997bffffafc974f36_ramissue.jpg!1)
ramissue
- 粉丝: 354
- 资源: 1487
上传资源 快速赚钱
我的内容管理 展开
我的资源 快来上传第一个资源
我的收益
登录查看自己的收益我的积分 登录查看自己的积分
我的C币 登录后查看C币余额
我的收藏
我的下载
下载帮助
![](https://csdnimg.cn/release/wenkucmsfe/public/img/voice.245cc511.png)
最新资源
- AirKiss技术详解:无线传递信息与智能家居连接
- Hibernate主键生成策略详解
- 操作系统实验:位示图法管理磁盘空闲空间
- JSON详解:数据交换的主流格式
- Win7安装Ubuntu双系统详细指南
- FPGA内部结构与工作原理探索
- 信用评分模型解析:WOE、IV与ROC
- 使用LVS+Keepalived构建高可用负载均衡集群
- 微信小程序驱动餐饮与服装业创新转型:便捷管理与低成本优势
- 机器学习入门指南:从基础到进阶
- 解决Win7 IIS配置错误500.22与0x80070032
- SQL-DFS:优化HDFS小文件存储的解决方案
- Hadoop、Hbase、Spark环境部署与主机配置详解
- Kisso:加密会话Cookie实现的单点登录SSO
- OpenCV读取与拼接多幅图像教程
- QT实战:轻松生成与解析JSON数据
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
![](https://img-home.csdnimg.cn/images/20220527035711.png)
![](https://img-home.csdnimg.cn/images/20220527035711.png)
![](https://img-home.csdnimg.cn/images/20220527035111.png)
安全验证
文档复制为VIP权益,开通VIP直接复制
![](https://csdnimg.cn/release/wenkucmsfe/public/img/green-success.6a4acb44.png)