更新时间：2021-07-09 19:34:36
封面
版权信息
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Downloading the color images of this book
Chapter 1. Getting Started with R and Machine Learning
Delving into the basics of R
Data structures in R
Working with functions
Controlling code flow
Advanced constructs
Next steps with R
Machine learning basics
Summary
Chapter 2. Let's Help Machines Learn
Understanding machine learning
Algorithms in machine learning
Families of algorithms
Chapter 3. Predicting Customer Shopping Trends with Market Basket Analysis
Detecting and predicting trends
Market basket analysis
Evaluating a product contingency matrix
Frequent itemset generation
Association rule mining
Chapter 4. Building a Product Recommendation System
Understanding recommendation systems
Issues with recommendation systems
Collaborative filters
Building a recommender engine
Production ready recommender engines
Chapter 5. Credit Risk Detection and Prediction – Descriptive Analytics
Types of analytics
Our next challenge
What is credit risk?
Getting the data
Data preprocessing
Data analysis and transformation
Next steps
Chapter 6. Credit Risk Detection and Prediction – Predictive Analytics
Predictive analytics
How to predict credit risk
Important concepts in predictive modeling
Feature selection
Modeling using logistic regression
Modeling using support vector machines
Modeling using decision trees
Modeling using random forests
Modeling using neural networks
Model comparison and selection
Chapter 7. Social Media Analysis – Analyzing Twitter Data
Social networks (Twitter)
Data mining @social networks
Getting started with Twitter APIs
Twitter data mining
Challenges with social network data mining
References
Chapter 8. Sentiment Analysis of Twitter Data
Understanding Sentiment Analysis
Sentiment analysis upon Tweets
Index
