[4] Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. Often the unknown event of interest is in the future...
Predictive analytics is the use of statistics and modeling techniques to determine future performance based on current and historical data.
Web and Network Data Science: Modeling Techniques in Predictive Analytics - 저자가 Thomas W. Miller인 eBook입니다. PC, Android, iOS 기기에서 Google Play 북 앱을 사용해 이 책을 읽어 보세요. 책을...
정가 : 131,620원, 판매가 : 107,920원 (18% 할인), YES포인트 : 5,400원 (5% 적립) + 마니아추가적립 · 5만원이상 구매 시 2천원 추가적립
Predictive modeling is built on two pillars: a set of predictive analytics techniques and tools and a process that standardizes how data analysts use them.
Learn how to use data, statistical methods, and machine learning to forecast future outcomes and trends in marketing with predictive analytics.
Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R - 저자가 Thomas W. Miller인 eBook입니다. PC, Android, iOS 기기에서 Google Play 북 앱을 사용해 이 책을 읽어...
Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python - 저자가 Thomas W. Miller인 eBook입니다. PC, Android, iOS 기기에서 Google Play 북 앱을 사용해 이 책을 읽어...
Predictive modeling is a mathematical process a that aims to predict future events or outcomes by analyzing relevant historical data. Learn how it's applied.
Predictive analytics predicts future outcomes by using historical data combined with statistical modeling, data mining techniques and machine learning.