{"id":82841,"date":"2018-02-21T10:14:24","date_gmt":"2018-02-21T10:14:24","guid":{"rendered":"http:\/\/www.esri.com\/arcgis-blog\/products\/product\/uncategorized\/predict-seagrass-habitats-with-machine-learning\/"},"modified":"2018-03-26T21:17:09","modified_gmt":"2018-03-26T21:17:09","slug":"predict-seagrass-habitats-with-machine-learning","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/data-management\/data-management\/predict-seagrass-habitats-with-machine-learning","title":{"rendered":"Predict Seagrass Habitats with Machine Learning"},"author":6581,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23851],"tags":[36351,36361,24341,36371,25581],"industry":[],"product":[36561,37031],"class_list":["post-82841","blog","type-blog","status-publish","format-standard","hentry","category-data-management","tag-arcgis-geostatistical-analyst","tag-florida","tag-python","tag-seagrass","tag-spatial-statistics","product-arcgis-pro","product-spatial-analyst"],"acf":{"short_description":"A new lesson from Learn ArcGIS allows you to identify locations worldwide for encouraging seagrass growth -- all based on a data from you...","flexible_content":[{"acf_fc_layout":"content","content":"<p><a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/02\/Turtle180x1101.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-100906\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/02\/Turtle180x1101.png\" alt=\"\" width=\"152\" height=\"112\" \/><\/a>A new lesson from <a href=\"https:\/\/learn.arcgis.com\/en\/\" target=\"_blank\">Learn ArcGIS<\/a> allows you to identify locations worldwide for encouraging seagrass growth &#8212; all based on a data from your small region\u00a0off Florida.<\/p>\n<p>In <a href=\"https:\/\/learn.arcgis.com\/en\/projects\/predict-seagrass-habitats-with-machine-learning\/\" target=\"_blank\">Predict Seagrass Habitats with Machine Learning<\/a>, you will assume the role of a marine ecologist who\u00a0wants to\u00a0find suitable habitats for seagrass\u00a0growth. Because seagrasses tend to grow in similar conditions, regardless of temperatures, your Florida data sample can be applied to the entire globe because of the predictive powers of machine learning taught in this lesson.<\/p>\n<p>First, you&#8217;ll create a training dataset with all the ocean variables that influence seagrass growth. You&#8217;ll put the variables into Python and use a random forest prediction model to determine which ocean area support seagrass growth. You&#8217;ll conclude by importing your predictions into ArcGIS Pro to locate areas where seagrass could grow.<\/p>\n"}],"authors":[{"ID":6581,"user_firstname":"John","user_lastname":"Berry","nickname":"john6214","user_nicename":"john6214","display_name":"John Berry","user_email":"JBerry@esri.com","user_url":"","user_registered":"2018-03-02 00:18:33","user_description":"Tweeting for LearnArcGIS is ssssoooo much fun! I'm John Berry, a recovering newspaper reporter and current product engineer for Learn ArcGIS. 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(And since becoming a dad in 2004, I've long mastered the fine art of dad humor, which -- properly timed -- can cause eyes to roll and laughs to start.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/03\/John_Berry_jpg.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}]},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Predict Seagrass Habitats with Machine Learning<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/data-management\/data-management\/predict-seagrass-habitats-with-machine-learning\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" 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