Machine learning--also known as data mining or data analytics-- is a fundamental part of data science. It is used by organizationsin a wide variety of arenas to turn raw data into actionableinformation. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in RapidMiner provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification,    visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques       End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions                   This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

• 关于产品：
• ● 正版保障：本网站隶属于中国国际图书贸易集团公司，确保所有图书都是100%正版。
• ● 环保纸张：进口图书大多使用的都是环保轻型张，颜色偏黄，重量比较轻。
• ● 毛边版：即书翻页的地方，故意做成了参差不齐的样子，一般为精装版，更具收藏价值。
• 
  关于退换货：
• 由于预订产品的特殊性，采购订单正式发订后，买方不得无故取消全部或部分产品的订购。
• 由于进口图书的特殊性，发生以下情况的，请直接拒收货物，由快递返回：
• ● 外包装破损/发错货/少发货/图书外观破损/图书配件不全（例如：光盘等）
  并请在工作日通过电话400-008-1110联系我们。
• 签收后，如发生以下情况，请在签收后的5个工作日内联系客服办理退换货：
• ● 缺页/错页/错印/脱线
• 
  关于发货时间：
• 一般情况下：
• ●【现货】 下单后48小时内由北京（库房）发出快递。
• ●【预订】【预售】下单后国外发货，到货时间预计5-8周左右，店铺默认中通快递，如需顺丰快递邮费到付。
• ● 需要开具发票的客户，发货时间可能在上述基础上再延后1-2个工作日（紧急发票需求，请联系010-68433105/3213）；
• ● 如遇其他特殊原因，对发货时间有影响的，我们会第一时间在网站公告，敬请留意。
• 
  关于到货时间：
• 由于进口图书入境入库后，都是委托第三方快递发货，所以我们只能保证在规定时间内发出，但无法为您保证确切的到货时间。
• ● 主要城市一般2-4天
• ● 偏远地区一般4-7天
• 
  关于接听咨询电话的时间：
• 010-68433105/3213正常接听咨询电话的时间为：周一至周五上午8：30～下午5：00，周六、日及法定节假日休息，将无法接听来电，敬请谅解。
• 其它时间您也可以通过邮件联系我们：customer@readgo.cn,工作日会优先处理。
• 
  关于快递：
• ● 已付款订单：主要由中通、宅急送负责派送，订单进度查询请拨打010-68433105/3213。