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所在平台: Udemy |
课程主页: https://www.udemy.com/course/case-studies-in-data-mining-with-r/
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课程名称：使用R进行数据挖掘的案例研究 课程概述：数据挖掘案例研究最初是作为三个独立的在线数据挖掘课程教授的。该课程通过三个案例研究，广泛展示数据挖掘的基本和扩展任务，这三个案例分别为：（1）预测藻华；（2）检测欺诈销售交易；（3）预测股票市场回报。这个包含三个课程、十五节课的实践性系列教学，展示了Luis Torgo极为有用的“使用R的数据挖掘”（DMwR）包和R软件的应用。课程中所有的屏幕显示内容都包含在内：所有R脚本、所有数据文件和R对象，以及所有R包的文档。无论是R软件的新手还是数据挖掘的初学者，您都能成功完成课程。 第一个案例研究“预测藻华”介绍了R软件中DMwR包的许多有用且独特的数据挖掘功能。该案例专注于数据预处理、探索性数据分析和预测模型构建的任务。对于完全新手的参与者，藻华预测案例的前两节（近4小时的视频和材料）提供了对R及RStudio的快速入门，以及基本输入输出数据和文本的技巧。 第二个拓展案例“检测欺诈交易”同样展示了DMwR包。该案例特定于一个常见的商业问题：如何从大量数据（本案例中有401,124条记录）中筛选出可疑数据条目或“异常值”？该问题相当非结构化，课程将探讨多种方法和技术，以区分“正常”或“正常交易”和“异常”、“可疑”或“欺诈交易”。该案例提供了许多替代建模方法，其中一些适用于监督学习，另一些则适用于非监督学习或半监督学习的数据场景。 第三个拓展案例“预测股票市场回报”探讨了自动股票交易系统的领域。这四节课涉及基于预测模型构建自动股票交易系统，利用每日股票报价数据以预测标准普尔500市场指数的未来回报。所得的预测结果与交易策略结合，用于制定市场买卖订单的决策。该案例探讨了基于数据观察时间序列的预测问题，以及在“现实世界”商业应用中将模型预测转化为决策和行动所面临的困难。
Case Studies in Data Mining was originally taught as three separate online data mining courses. We examine three case studies which together present a broad-based tour of the basic and extended tasks of data mining in three different domains: (1) predicting algae blooms; (2) detecting fraudulent sales transactions; and (3) predicting stock market returns. The cumulative "hands-on" 3-course fifteen sessions showcase the use of Luis Torgo's amazingly useful "Data Mining with R" (DMwR) package and R software. Everything that you see on-screen is included with the course: all of the R scripts; all of the data files and R objects used and/or referenced; as well as all of the R packages' documentation. You can be new to R software and/or to data mining and be successful in completing the course. The first case study, Predicting Algae Blooms, provides instruction regarding the many useful, unique data mining functions contained in the R software 'DMwR' package. For the algae blooms prediction case, we specifically look at the tasks of data pre-processing, exploratory data analysis, and predictive model construction. For individuals completely new to R, the first two sessions of the algae blooms case (almost 4 hours of video and materials) provide an accelerated introduction to the use of R and RStudio and to basic techniques for inputting and outputting data and text. Detecting Fraudulent Transactions is the second extended data mining case study that showcases the DMwR (Data Mining with R) package. The case is specific but may be generalized to a common business problem: How does one sift through mountains of data (401,124 records, in this case) and identify suspicious data entries, or "outliers"? The case problem is very unstructured, and walks through a wide variety of approaches and techniques in the attempt to discriminate the "normal", or "ok" transactions, from the abnormal, suspicious, or "fraudulent" transactions. This case presents a large number of alternative modeling approaches, some of which are appropriate for supervised, some for unsupervised, and some for semi-supervised data scenarios. The third extended case, Predicting Stock Market Returns is a data mining case study addressing the domain of automatic stock trading systems. These four sessions address the tasks of building an automated stock trading system based on prediction models that utilize daily stock quote data. The goal is to predict future returns for the S & P 500 market index. The resulting predictions are used together with a trading strategy to make decisions about generating market buy and sell orders. The case examines prediction problems that stem from the time ordering among data observations, that is, from the use of time series data. It also exemplifies the difficulties involved in translating model predictions into decisions and actions in the context of 'real-world' business applications.
