{"id":7657,"date":"2016-05-25T11:05:09","date_gmt":"2016-05-25T15:05:09","guid":{"rendered":"http:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=7657"},"modified":"2016-07-11T16:02:20","modified_gmt":"2016-07-11T20:02:20","slug":"feature-engineering-within-the-predictive-analytics-process-part-one","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/feature-engineering-within-the-predictive-analytics-process-part-one\/7657\/","title":{"rendered":"Feature Engineering within the Predictive Analytics Process &#8212; Part One"},"content":{"rendered":"What is Feature Engineering One of the growing discussions and debates within the data science community is the determination of inputs or variables that should be included in any predictive analytics algorithm. This type of process is more commonly referred to as feature engineering. Historically, this process is typically the most time-consuming element in building any predictive analytics solution as the practitioner can usually create hundreds of variables that might be considered in a predictive model. But what is involved in this process. It is not simply the consumption of all this information into a data lake and <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/feature-engineering-within-the-predictive-analytics-process-part-one\/7657\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>What is Feature Engineering One of the growing discussions and debates within the data science community is the determination of inputs or variables that should be included in any predictive analytics algorithm. This type of process is more commonly referred to as feature engineering. Historically, this process is typically the most time-consuming element in building [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[3],"tags":[42,47,8],"class_list":["post-7657","post","type-post","status-publish","format-standard","hentry","category-leading-stories","tag-big-data","tag-data-scientist","tag-predictive-analytics"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/7657","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=7657"}],"version-history":[{"count":3,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/7657\/revisions"}],"predecessor-version":[{"id":7660,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/7657\/revisions\/7660"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=7657"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=7657"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=7657"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}