Python数据挖掘实战：构建预测模型与应用

5星 · 超过95%的资源需积分: 10 199 浏览量更新于2024-07-21 2 收藏 3.92MB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

资源详情

资源推荐

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

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 classication, 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 Afnity 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 workows 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.

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 sufce, 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 denitely 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

ramissue

粉丝: 354
资源: 1487

Python数据挖掘实战：构建预测模型与应用

Learning Data Mining With Python book 代码及实例数据

Learning Data Mining with Python pdf 2015 0分

Learning Data Mining with Python.pdf

Machine Learning specific learning route

python utils

国外文本挖掘应用参考文献

“Python”、“Java”、“MachineLearning”、“DataMining”四个单词必须为双引号中给出的形式，否则拼写不正确；

用python完成机器学习实验鸢尾花数据集SVM分类需要的参考文献

dbscan python

计算机毕设英文参考文献

机器学习 大数据 书籍

apriori算法python实现

'DataFrame' object has no attribute 'min_support

Knn算法的基本介绍以及使用的语言环境介绍 2、算法的运行举例（截图或者图表）以及性能比较 3、算法的改进、变种以及其解决了什么具体的现实问题 要求：5篇参考文献以上

有什么关于机器学习推荐的好书

tkinter论文推荐系统

写一份实验：基于Apriori算法的图书推荐的实现

帮我写一段固定短语抽取的代码

最新资源

机器学习大数据书籍

Knn算法的基本介绍以及使用的语言环境介绍 2、算法的运行举例（截图或者图表）以及性能比较 3、算法的改进、变种以及其解决了什么具体的现实问题要求：5篇参考文献以上