http://git-wip-us.apache.org/repos/asf/incubator-predictionio-site/blob/32eb5edf/gallery/template-gallery/index.html ---------------------------------------------------------------------- diff --git a/gallery/template-gallery/index.html b/gallery/template-gallery/index.html new file mode 100644 index 0000000..75d2193 --- /dev/null +++ b/gallery/template-gallery/index.html @@ -0,0 +1,6 @@ +<!DOCTYPE html><html><head><title>Engine Template Gallery</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta class="swiftype" name="title" data-type="string" content="Engine Template Gallery"/><link rel="canonical" href="https://predictionio.incubator.apache.org/gallery/template-gallery/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link 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id="pill-wrapper"><a class="pill left" href="/gallery/template-gallery">TEMPLATES</a> <a class="pill right" href="//github.com/apache/incubator-predictionio/">OPEN SOURCE</a></div></div><img class="mobile-search-bar-toggler hidden-md hidden-lg" src="/images/icons/search-glass-704bd4ff.png"/></div></div></div></header><div id="search-bar-row-wrapper"><div class="container-fluid" id="search-bar-row"><div class="row"><div class="col-md-9 col-sm-11 col-xs-1 1"><div class="hidden-md hidden-lg" id="mobile-page-heading-wrapper"><p>PredictionIO Docs</p><h4>Browse</h4></div><h4 class="hidden-sm hidden-xs">PredictionIO Docs</h4></div><div class="col-md-3 col-sm-1 col-xs-1 hidden-md hidden-lg"><img id="left-menu-indicator" src="/images/icons/down-arrow-dfe9f7fe.png"/></div><div class="col-md-3 col-sm-12 col-xs-12 swiftype-wrapper"><div class="swiftype"><form class="search-form"><img class="search-box-toggler hidden-xs hidden-sm" src="/images/icons/search-glass-704bd4ff.png"/><div class="search-box"><img src="/images/icons/search-glass-704bd4ff.png"/><input type="text" id="st-search-input" class="st-search-input" placeholder="Search Doc..."/></div><img class="swiftype-row-hider hidden-md hidden-lg" src="/images/icons/drawer-toggle-active-fcbef12a.png"/></form></div></div><div class="mobile-left-menu-toggler hidden-md hidden-lg"></div></div></div></div><div id="page" class="container-fluid"><div class="row"><div id="left-menu-wrapper" class="co l-md-3"><nav id="nav-main"><ul><li class="level-1"><a class="expandible" href="/"><span>Apache PredictionIO (incubating) Documentation</span></a><ul><li class="level-2"><a class="final" href="/"><span>Welcome to Apache PredictionIO (incubating)</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Getting Started</span></a><ul><li class="level-2"><a class="final" href="/start/"><span>A Quick Intro</span></a></li><li class="level-2"><a class="final" href="/install/"><span>Installing Apache PredictionIO (incubating)</span></a></li><li class="level-2"><a class="final" href="/start/download/"><span>Downloading an Engine Template</span></a></li><li class="level-2"><a class="final" href="/start/deploy/"><span>Deploying Your First Engine</span></a></li><li class="level-2"><a class="final" href="/start/customize/"><span>Customizing the Engine</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Integrating with Your App</span></a><ul> <li class="level-2"><a class="final" href="/appintegration/"><span>App Integration Overview</span></a></li><li class="level-2"><a class="expandible" href="/sdk/"><span>List of SDKs</span></a><ul><li class="level-3"><a class="final" href="/sdk/java/"><span>Java & Android SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/php/"><span>PHP SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/python/"><span>Python SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/ruby/"><span>Ruby SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/community/"><span>Community Powered SDKs</span></a></li></ul></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Deploying an Engine</span></a><ul><li class="level-2"><a class="final" href="/deploy/"><span>Deploying as a Web Service</span></a></li><li class="level-2"><a class="final" href="/cli/#engine-commands"><span>Engine Command-line Interface</span></a></li><li class="level- 2"><a class="final" href="/batchpredict/"><span>Batch Predictions</span></a></li><li class="level-2"><a class="final" href="/deploy/monitoring/"><span>Monitoring Engine</span></a></li><li class="level-2"><a class="final" href="/deploy/engineparams/"><span>Setting Engine Parameters</span></a></li><li class="level-2"><a class="final" href="/deploy/enginevariants/"><span>Deploying Multiple Engine Variants</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Customizing an Engine</span></a><ul><li class="level-2"><a class="final" href="/customize/"><span>Learning DASE</span></a></li><li class="level-2"><a class="final" href="/customize/dase/"><span>Implement DASE</span></a></li><li class="level-2"><a class="final" href="/customize/troubleshooting/"><span>Troubleshooting Engine Development</span></a></li><li class="level-2"><a class="final" href="/api/current/#package"><span>Engine Scala APIs</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Collecting and Analyzing Data</span></a><ul><li class="level-2"><a class="final" href="/datacollection/"><span>Event Server Overview</span></a></li><li class="level-2"><a class="final" href="/cli/#event-server-commands"><span>Event Server Command-line Interface</span></a></li><li class="level-2"><a class="final" href="/datacollection/eventapi/"><span>Collecting Data with REST/SDKs</span></a></li><li class="level-2"><a class="final" href="/datacollection/eventmodel/"><span>Events Modeling</span></a></li><li class="level-2"><a class="final" href="/datacollection/webhooks/"><span>Unifying Multichannel Data with Webhooks</span></a></li><li class="level-2"><a class="final" href="/datacollection/channel/"><span>Channel</span></a></li><li class="level-2"><a class="final" href="/datacollection/batchimport/"><span>Importing Data in Batch</span></a></li><li class="level-2"><a class="final" href="/datacollection/analytics/"><span>Using Analytics Tools</span></a></li></ul></li><l i class="level-1"><a class="expandible" href="#"><span>Choosing an Algorithm(s)</span></a><ul><li class="level-2"><a class="final" href="/algorithm/"><span>Built-in Algorithm Libraries</span></a></li><li class="level-2"><a class="final" href="/algorithm/switch/"><span>Switching to Another Algorithm</span></a></li><li class="level-2"><a class="final" href="/algorithm/multiple/"><span>Combining Multiple Algorithms</span></a></li><li class="level-2"><a class="final" href="/algorithm/custom/"><span>Adding Your Own Algorithms</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>ML Tuning and Evaluation</span></a><ul><li class="level-2"><a class="final" href="/evaluation/"><span>Overview</span></a></li><li class="level-2"><a class="final" href="/evaluation/paramtuning/"><span>Hyperparameter Tuning</span></a></li><li class="level-2"><a class="final" href="/evaluation/evaluationdashboard/"><span>Evaluation Dashboard</span></a></li><li class="level-2"><a class="f inal" href="/evaluation/metricchoose/"><span>Choosing Evaluation Metrics</span></a></li><li class="level-2"><a class="final" href="/evaluation/metricbuild/"><span>Building Evaluation Metrics</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>System Architecture</span></a><ul><li class="level-2"><a class="final" href="/system/"><span>Architecture Overview</span></a></li><li class="level-2"><a class="final" href="/system/anotherdatastore/"><span>Using Another Data Store</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>PredictionIO Official Templates</span></a><ul><li class="level-2"><a class="final" href="/templates/"><span>Intro</span></a></li><li class="level-2"><a class="expandible" href="#"><span>Recommendation</span></a><ul><li class="level-3"><a class="final" href="/templates/recommendation/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/dase/"><span >DASE</span></a></li><li class="level-3"><a class="final" >href="/templates/recommendation/evaluation/"><span>Evaluation >Explained</span></a></li><li class="level-3"><a class="final" >href="/templates/recommendation/how-to/"><span>How-To</span></a></li><li >class="level-3"><a class="final" >href="/templates/recommendation/reading-custom-events/"><span>Read Custom >Events</span></a></li><li class="level-3"><a class="final" >href="/templates/recommendation/customize-data-prep/"><span>Customize Data >Preparator</span></a></li><li class="level-3"><a class="final" >href="/templates/recommendation/customize-serving/"><span>Customize >Serving</span></a></li><li class="level-3"><a class="final" >href="/templates/recommendation/training-with-implicit-preference/"><span>Train > with Implicit Preference</span></a></li><li class="level-3"><a class="final" >href="/templates/recommendation/blacklist-items/"><span>Filter Recommended >Items by Blacklist in Query</span></a></li><li class="level-3"><a class="final " href="/templates/recommendation/batch-evaluator/"><span>Batch Persistable Evaluator</span></a></li></ul></li><li class="level-2"><a class="expandible" href="#"><span>E-Commerce Recommendation</span></a><ul><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/train-with-rate-event/"><span>Train with Rate Event</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust Score</span></a></li></ul></li><li class="level-2"><a class="expandible" href="#"><span>Similar Product</span></a><ul><li class="level-3"><a class="final" href="/templates/similarproduct/q uickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple Events and Multiple Algorithms</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/return-item-properties/"><span>Returns Item Properties</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate Event</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events for Users</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/recommended-user/"><span>Recommend Users</span></a></li></ul></li><li class="level-2"><a class="expandib le" href="#"><span>Classification</span></a><ul><li class="level-3"><a class="final" href="/templates/classification/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/classification/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/classification/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/classification/add-algorithm/"><span>Use Alternative Algorithm</span></a></li><li class="level-3"><a class="final" href="/templates/classification/reading-custom-properties/"><span>Read Custom Properties</span></a></li></ul></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li class="level-2"><a class="final active" href="/gallery/template-gallery/"><span>Browse</span></a></li><li class="level-2"><a class="final" href="/community/submit-template/"><span>Submit your Engine as a Template</span></a></li></ul>< /li><li class="level-1"><a class="expandible" href="#"><span>Demo Tutorials</span></a><ul><li class="level-2"><a class="final" href="/demo/tapster/"><span>Comics Recommendation Demo</span></a></li><li class="level-2"><a class="final" href="/demo/community/"><span>Community Contributed Demo</span></a></li><li class="level-2"><a class="final" href="/demo/textclassification/"><span>Text Classification Engine Tutorial</span></a></li></ul></li><li class="level-1"><a class="expandible" href="/community/"><span>Getting Involved</span></a><ul><li class="level-2"><a class="final" href="/community/contribute-code/"><span>Contribute Code</span></a></li><li class="level-2"><a class="final" href="/community/contribute-documentation/"><span>Contribute Documentation</span></a></li><li class="level-2"><a class="final" href="/community/contribute-sdk/"><span>Contribute a SDK</span></a></li><li class="level-2"><a class="final" href="/community/contribute-webhook/"><span>Contribute a Webhook</span></a ></li><li class="level-2"><a class="final" >href="/community/projects/"><span>Community >Projects</span></a></li></ul></li><li class="level-1"><a class="expandible" >href="#"><span>Getting Help</span></a><ul><li class="level-2"><a >class="final" href="/resources/faq/"><span>FAQs</span></a></li><li >class="level-2"><a class="final" >href="/support/"><span>Support</span></a></li></ul></li><li >class="level-1"><a class="expandible" >href="#"><span>Resources</span></a><ul><li class="level-2"><a class="final" >href="/resources/release/"><span>Release Cadence</span></a></li><li >class="level-2"><a class="final" href="/resources/intellij/"><span>Developing >Engines with IntelliJ IDEA</span></a></li><li class="level-2"><a >class="final" href="/resources/upgrade/"><span>Upgrade >Instructions</span></a></li><li class="level-2"><a class="final" >href="/resources/glossary/"><span>Glossary</span></a></li></ul></li><li >class="level-1"><a class="expandible" href="#"><span>Apache Software >Foundation</span></a><u l><li class="level-2"><a class="final" href="https://www.apache.org/";><span>Apache Homepage</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/licenses/";><span>License</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/sponsorship.html";><span>Sponsorship</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/thanks.html";><span>Thanks</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/security/";><span>Security</span></a></li></ul></li></ul></nav></div><div class="col-md-9 col-sm-12"><div class="content-header hidden-md hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">Engine Template Gallery</a><span class="spacer">></span></li><li><span class="last">Browse</span></li></ul></div><div id="page-title"><h1>Engine Template Gallery</h1></div></div><div id="table-of-content-wrapper"><a id="edit-page-link" href="https://g ithub.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/gallery/template-gallery.html.md"><img src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">Engine Template Gallery</a><span class="spacer">></span></li><li><span class="last">Browse</span></li></ul></div><div id="page-title"><h1>Engine Template Gallery</h1></div></div><div class="content"><p>Pick a tab for the type of template you are looking for. Some still need to be ported (a simple process) to Apache PIO and these are marked. Also see each Template description for special support instructions.</p><div class="tabs"> <ul class="control"> <li data-lang=""><a href="#tab-cff27152-f238-4b0f-9b91-ddf9fe36069a">Recommenders</a></li> <li data-lang=""><a href="#tab-66be5387-65e9-4b50-99d1-31a6a6a02ccd">Classification</a></li> <li data-lang=""><a href="#tab-0a574b00-9cf3-46b9-aeff-4 d25511ae744">Regression</a></li> <li data-lang=""><a href="#tab-27b0e573-928b-4956-81e8-a8bce4a7682c">NLP</a></li> <li data-lang=""><a href="#tab-656ff594-05e8-4798-beb7-c8afb9b37ebd">Clustering</a></li> <li data-lang=""><a href="#tab-a5566745-e45e-464c-9acb-f8ac2d5330db">Similarity</a></li> <li data-lang=""><a href="#tab-14574064-995c-430c-bacc-9e4cc128ecc3">Other</a></li> </ul> <div data-tab="Recommenders" id="tab-cff27152-f238-4b0f-9b91-ddf9fe36069a"> <h3><a href="https://github.com/actionml/universal-recommender";>The Universal Recommender</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=actionml&repo=universal-recommender&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Use for: </p> <ul class="tab-list"> <li class="tab-list-element">Personalized recommendationsâuser-based</li> <li class="tab-list-element">Similar itemsâitem-based</li> <li class="tab-list-element">Viewed this bought thatâi tem-based cross-action</li> <li class="tab-list-element">Popular Items and User-defined ranking</li> <li class="tab-list-element">Item-set recommendations for complimentarty purchases or shopping cartsâitem-set-based</li> <li class="tab-list-element">Hybrid collaborative filtering and content based recommendationsâlimited content-based</li> <li class-tab-list-element>Business rules</li> </ul> <p>The name "Universal" refers to the use of this template in virtually any case that calls for recommendations - ecommerce, news, videos, virtually anywhere user behavioral data is known. This recommender uses the new <a href="http://mahout.apache.org/users/algorithms/intro-cooccurrence-spark.html";>Cross-Occurrence (CCO) algorithm</a> to auto-correlate different user actions (clickstream data), profile data, contextual information (location, device), and some content types to make better recommendations. It also implements flexible filters and boosts for implementing business rules.</p> <p >Support: <a href="https://groups.google.com/forum/#!forum/actionml-user";>The >Universal Recommender user group</a></p> <br> <table> <tr> <th>Type</th> ><th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> ><th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> ><td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> ><td>0.10.0-incubating</td> <td>already compatible</td> </tr> </table> <br> ><h3><a >href="https://github.com/apache/incubator-predictionio-template-recommender";>Recommendation</a></h3> > <iframe >src="https://ghbtns.com/github-btn.html?user=apache&repo=incubator-predictionio-template-recommender&type=star&count=true"; > frameborder="0" align="middle" scrolling="0" width="170px" >height="20px"></iframe> <p> An engine template is an almost-complete >implementation of an engine. PredictionIO's Recommendation Engine Template >has integrated Apache Spark MLlib's Collaborative Filtering algorithm by >default. You can customize it easily to f it your specific needs. </p> <p>Support: <a href="http://predictionio.incubator.apache.org/support/";>Apache PredictionIO mailing lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> <td>already compatible</td> </tr> </table> <br> <h3><a href="https://github.com/apache/incubator-predictionio-template-ecom-recommender";>E-Commerce Recommendation</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=apache&repo=incubator-predictionio-template-ecom-recommender&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This engine template provides personalized recommendation for e-commerce applications with the following features by default: </p> <ul class="tab-list"> <li class="tab-list-element">Exc lude out-of-stock items</li> <li class="tab-list-element">Provide recommendation to new users who sign up after the model is trained</li> <li class="tab-list-element">Recommend unseen items only (configurable)</li> <li class="tab-list-element">Recommend popular items if no information about the user is available (added in template version v0.4.0)</li> </ul> <p>Support: <a href="http://predictionio.incubator.apache.org/support/";>Apache PredictionIO mailing lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.10.0-incubating</td> <td>already compatible</td> </tr> </table> <br> <h3><a href="https://github.com/apache/incubator-predictionio-template-similar-product";>Similar Product</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=apache&repo=incubator-predictionio-template- similar-product&type=star&count=true" frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This engine template recommends products that are "similar" to the input product(s). Similarity is not defined by user or item attributes but by users' previous actions. By default, it uses 'view' action such that product A and B are considered similar if most users who view A also view B. The template can be customized to support other action types such as buy, rate, like..etc </p> <p>Support: <a href="http://predictionio.incubator.apache.org/support/";>Apache PredictionIO mailing lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> <td>already compatible</td> </tr> </table> <br> <h3><a href="https://github.com/apache/incubator-predi ctionio-template-java-ecom-recommender">E-Commerce Recommendation (Java)</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=apache&repo=incubator-predictionio-template-java-ecom-recommender&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This engine template provides personalized recommendation for e-commerce applications with the following features by default: </p> <ul class="tab-list"> <li class="tab-list-element">Exclude out-of-stock items</li> <li class="tab-list-element">Provide recommendation to new users who sign up after the model is trained</li> <li class="tab-list-element">Recommend unseen items only (configurable)</li> <li class="tab-list-element">Recommend popular items if no information about the user is available</li> </ul> <p>Support: <a href="http://predictionio.incubator.apache.org/support/";>Apache PredictionIO mailing lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> < th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Java</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.3</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/PredictionIO/template-scala-parallel-productranking";>Product Ranking</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=PredictionIO&repo=template-scala-parallel-productranking&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This engine template sorts a list of products for a user based on his/her preference. This is ideal for personalizing the display order of product page, catalog, or menu items if you have large number of options. It creates engagement and early conversion by placing products that a user prefers on the top. </p> <p>Support: </p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</t h> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/PredictionIO/template-scala-parallel-complementarypurchase";>Complementary Purchase</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=PredictionIO&repo=template-scala-parallel-complementarypurchase&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This engine template recommends the complementary items which most user frequently buy at the same time with one or more items in the query. </p> <p>Support: </p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td> requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/vaibhavist/template-scala-parallel-recommendation";>Music Recommendations</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=vaibhavist&repo=template-scala-parallel-recommendation&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This is very similar to music recommendations template. It is integrated with all the events a music application can have such as song played, liked, downloaded, purchased, etc. </p> <p>Support: <a href="https://github.com/vaibhavist/template-scala-parallel-recommendation/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a h ref="https://github.com/vngrs/template-scala-parallel-viewedthenbought";>Viewed This Bought That</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=vngrs&repo=template-scala-parallel-viewedthenbought&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This Engine uses co-occurence algorithm to match viewed items to bought items. Using this engine you may predict which item the user will buy, given the item(s) browsed. </p> <p>Support: <a href="https://github.com/vngrs/template-scala-parallel-viewedthenbought/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/goliasz/pio-template-fpm";>Frequent Pattern Mining</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=goliasz&repo=pio-template-fpm&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Template uses FP Growth algorithm allowing to mine for frequent patterns. Template returns subsequent items together with confidence score. Sometimes used as a shopping cart recommender but has other uses. </p> <p>Support: <a href="https://github.com/goliasz/pio-template-fpm/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/ramaboo/template-scala-parallel-similarproduct-with-rating";>Similar Product with Rating</a></h3> <iframe src="https://ghbtns.com/github-btn.h tml?user=ramaboo&repo=template-scala-parallel-similarproduct-with-rating&type=star&count=true" frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Similar product template with rating support! Used for the MovieLens Demo. </p> <p>Support: <a href="https://github.com/ramaboo/template-scala-parallel-similarproduct-with-rating/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>beta</td> <td>0.9.0</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/goliasz/pio-template-fpm";>Frequent Pattern Mining</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=goliasz&repo=pio-template-fpm&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Te mplate uses FP Growth algorithm allowing to mine for frequent patterns. Template returns subsequent items together with confidence score. </p> <p>Support: <a href="https://github.com/goliasz/pio-template-fpm/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> </tr> </table> <br> </div> <div data-tab="Classification" id="tab-66be5387-65e9-4b50-99d1-31a6a6a02ccd"> <h3><a href="https://github.com/apache/incubator-predictionio-template-attribute-based-classifier";>Classification</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=apache&repo=incubator-predictionio-template-attribute-based-classifier&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> An engin e template is an almost-complete implementation of an engine. PredictionIO's Classification Engine Template has integrated Apache Spark MLlib's Naive Bayes algorithm by default. </p> <p>Support: <a href="http://predictionio.incubator.apache.org/support/";>Apache PredictionIO mailing lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>already compatible</td> </tr> </table> <br> <h3><a href="https://github.com/haricharan123/PredictionIo-lingpipe-MultiLabelClassification";>Classification</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=haricharan123&repo=PredictionIo-lingpipe-MultiLabelClassification&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This engine template is an almost-complete i mplementation of an engine meant to used with PredictionIO. This Multi-label Classification Engine Template has integrated LingPipe (http://alias-i.com/lingpipe/) algorithm by default. </p> <p>Support: </p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Java</td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.9.5</td> <td>already compatible</td> </tr> </table> <br> <h3><a href="https://github.com/PredictionIO/template-scala-parallel-leadscoring";>Lead Scoring</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=PredictionIO&repo=template-scala-parallel-leadscoring&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This engine template predicts the probability of an user will convert (conversion event by user) in the current session. </p> <p>Support: </p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/apache/incubator-predictionio-template-text-classifier";>Text Classification</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=apache&repo=incubator-predictionio-template-text-classifier&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Use this engine for general text classification purposes. Uses OpenNLP library for text vectorization, includes t.f.-i.d.f.-based feature transformation and reduction, and uses Spark MLLib's Multinomial Naive Bayes implementation for classification. </p> <p>Support: <a href="https://github.com/apache/incubator-predictionio-template-text-classifier/issues";>Github i ssues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/andrewwuan/PredictionIO-Churn-Prediction-H2O-Sparkling-Water";>Churn Prediction - H2O Sparkling Water</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=andrewwuan&repo=PredictionIO-Churn-Prediction-H2O-Sparkling-Water&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This is an engine template with Sparkling Water integration. The goal is to use Deep Learning algorithm to predict the churn rate for a phone carrier's customers. </p> <p>Support: <a href="https://github.com/andrewwuan/PredictionIO-Churn-Prediction-H2O-Sparkling-Water/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/detrevid/predictionio-template-classification-dl4j";>Classification Deeplearning4j</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=detrevid&repo=predictionio-template-classification-dl4j&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> A classification engine template that uses Deeplearning4j library. </p> <p>Support: <a href="https://github.com/detrevid/predictionio-template-classification-dl4j/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/EmergentOrder/template-scala-probabilistic-classifier-batch-lbfgs";>Probabilistic Classifier (Logistic Regression w/ LBFGS)</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=EmergentOrder&repo=template-scala-probabilistic-classifier-batch-lbfgs&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> A PredictionIO engine template using logistic regression (trained with limited-memory BFGS ) with raw (probabilistic) outputs. </p> <p>Support: <a href="https://github.com/EmergentOrder/template-scala-probabilistic-classifier-batch-lbfgs/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td >Scala</td> <td>MIT License</td> <td>alpha</td> <td>0.9.2</td> <td>requires >conversion</td> </tr> </table> <br> <h3><a >href="https://github.com/chrischris292/template-classification-opennlp";>Document > Classification with OpenNLP</a></h3> <iframe >src="https://ghbtns.com/github-btn.html?user=chrischris292&repo=template-classification-opennlp&type=star&count=true"; > frameborder="0" align="middle" scrolling="0" width="170px" >height="20px"></iframe> <p> Document Classification template with OpenNLP >GISModel. </p> <p>Support: <a >href="https://github.com/chrischris292/template-classification-opennlp/issues";>Github > issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> ><th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO >Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> ><td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.0</td> <td>requires >conversion</td> </tr> </table> <br> <h3><a >href="https://github.com/harry5z/template-circuit-cla ssification-sparkling-water">Circuit End Use Classification</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=harry5z&repo=template-circuit-classification-sparkling-water&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> A classification engine template that uses machine learning models trained with sample circuit energy consumption data and end usage to predict the end use of a circuit by its energy consumption history. </p> <p>Support: <a href="https://github.com/harry5z/template-circuit-classification-sparkling-water/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.1</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/ailurus1991/GBRT_Template_Pr edictionIO">GBRT_Classification</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=ailurus1991&repo=GBRT_Template_PredictionIO&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> The Gradient-Boosted Regression Trees(GBRT) for classification. </p> <p>Support: <a href="https://github.com/ailurus1991/GBRT_Template_PredictionIO/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/mohanaprasad1994/PredictionIO-MLlib-Decision-Trees-Template";>MLlib-Decision-Trees-Template</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=mohanaprasad1994&repo=PredictionIO-MLlib-Decision-Trees-Template&type =star&count=true" frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> An engine template is an almost-complete implementation of an engine. This is a classification engine template which has integrated Apache Spark MLlib's Decision tree algorithm by default. </p> <p>Support: <a href="https://github.com/mohanaprasad1994/PredictionIO-MLlib-Decision-Trees-Template/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.0</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/jimmyywu/predictionio-template-classification-dl4j-multilayer-network";>Classification with MultiLayerNetwork</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=jimmyywu&repo=predictionio-template-classification-dl4j-mul tilayer-network&type=star&count=true" frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This engine template integrates the MultiLayerNetwork implementation from the Deeplearning4j library into PredictionIO. In this template, we use PredictionIO to classify the widely-known IRIS flower dataset by constructing a deep-belief net. </p> <p>Support: <a href="https://github.com/jimmyywu/predictionio-template-classification-dl4j-multilayer-network/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.0</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/thomasste/template-scala-parallel-dl4j-rntn";>Deeplearning4j RNTN</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=thomasste&repo=t emplate-scala-parallel-dl4j-rntn&type=star&count=true" frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Recursive Neural Tensor Network algorithm is supervised learning algorithm used to predict sentiment of sentences. This template is based on deeplearning4j RNTN example: https://github.com/SkymindIO/deeplearning4j-nlp-examples/tree/master/src/main/java/org/deeplearning4j/rottentomatoes/rntn. It's goal is to show how to integrate deeplearning4j library with PredictionIO. </p> <p>Support: <a href="https://github.com/thomasste/template-scala-parallel-dl4j-rntn/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/singsanj/classifier-kafka -streaming-template">classifier-kafka-streaming-template</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=singsanj&repo=classifier-kafka-streaming-template&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> The template will provide a simple integration of DASE with kafka using spark streaming capabilites in order to play around with real time notification, messages .. </p> <p>Support: <a href="https://github.com/singsanj/classifier-kafka-streaming-template/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>-</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/peoplehum/BagOfWords_SentimentAnalysis_Template";>Sentiment Analysis - Bag of Words Model</a></h3 > <iframe > src="https://ghbtns.com/github-btn.html?user=peoplehum&repo=BagOfWords_SentimentAnalysis_Template&type=star&count=true"; > frameborder="0" align="middle" scrolling="0" width="170px" > height="20px"></iframe> <p> This sentiment analysis template uses a bag of > words model. Given text, the engine will return sentiment as 1.0 (positive) > or 0.0 (negative) along with scores indicating how +ve or -ve it is. </p> > <p>Support: <a > href="https://github.com/peoplehum/BagOfWords_SentimentAnalysis_Template/issues";>Github > issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> > <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO > Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> > <td>Apache Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> > <td>already compatible</td> </tr> </table> <br> </div> <div > data-tab="Regression" id="tab-0a574b00-9cf3-46b9-aeff-4d25511ae744"> <h3><a > href="https://github.com/goliasz/pio-template-sr";>Survi val Regression</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=goliasz&repo=pio-template-sr&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Survival regression template is based on brand new Spark 1.6 AFT (accelerated failure time) survival analysis algorithm. There are interesting applications of survival analysis like: </p> <ul class="tab-list"> <li class="tab-list-element">Business Planning : Profiling customers who has a higher survival rate and make strategy accordingly.</li> <li class="tab-list-element">Lifetime Value Prediction : Engage with customers according to their lifetime value</li> <li class="tab-list-element">Active customers : Predict when the customer will be active for the next time and take interventions accordingly. * Campaign evaluation : Monitor effect of campaign on the survival rate of customers.</li> </ul> Source: http://www.analyticsvidhya.com/blog/2014/04/survival-analy sis-model-you/ <p>Support: <a href="http://www.analyticsvidhya.com/blog/2014/04/survival-analysis-model-you/";>Blog post</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>beta</td> <td>0.9.5</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/BensonQiu/predictionio-template-recommendation-sparklingwater";>Sparkling Water-Deep Learning Energy Forecasting</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=BensonQiu&repo=predictionio-template-recommendation-sparklingwater&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This Engine Template demonstrates an energy forecasting engine. It integrates Deep Learning from the Sparkling Water library to perform energy analysis. We can query the circuit and time, and return predicted energy usage. </p> <p>Support: <a href="https://github.com/BensonQiu/predictionio-template-recommendation-sparklingwater/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/detrevid/predictionio-load-forecasting";>Electric Load Forecasting</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=detrevid&repo=predictionio-load-forecasting&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This is a PredictionIO engine for electric load forecasting. The engine is using linear regression with stochastic gradient descent from Spark MLlib. </p> <p>Support: <a href="https://github.c om/detrevid/predictionio-load-forecasting/issues">Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/RAditi/PredictionIO-MLLib-LinReg-Template";>MLLib-LinearRegression</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=RAditi&repo=PredictionIO-MLLib-LinReg-Template&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This template uses the linear regression with stochastic gradient descent algorithm from MLLib to make predictions on real-valued data based on features (explanatory variables) </p> <p>Support: <a href="https://github.com/RAditi/PredictionIO-MLLib-LinReg-Template/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.1</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/mgcdanny/pio-linear-regression-bfgs";>Linear Regression BFGS</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=mgcdanny&repo=pio-linear-regression-bfgs&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Modeling the relationship between a dependent variable, y, and one or more explanatory variables, denoted X. </p> <p>Support: </p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>beta</td> <td>0.10.0</td> <td></t d> </tr> </table> <br> </div> <div data-tab="NLP" id="tab-27b0e573-928b-4956-81e8-a8bce4a7682c"> <h3><a href="https://github.com/goliasz/pio-template-text-similarity";>Cstablo-template-text-similarity-classification</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=goliasz&repo=pio-template-text-similarity&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Text similarity engine based on Word2Vec algorithm. Builds vectors of full documents in training phase. Finds similar documents in query phase. </p> <p>Support: <a href="https://github.com/goliasz/pio-template-text-similarity/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> </tr> </table> <br> <h3 ><a >href="https://github.com/peoplehum/template-Labelling-Topics-with-wikipedia";>Topic > Labelling with Wikipedia</a></h3> <iframe >src="https://ghbtns.com/github-btn.html?user=peoplehum&repo=template-Labelling-Topics-with-wikipedia&type=star&count=true"; > frameborder="0" align="middle" scrolling="0" width="170px" >height="20px"></iframe> <p> This template will label topics (e.g. topic >generated through LDA topic modeling) with relevant category by referring to >Wikipedia as a knowledge base. </p> <p>Support: <a >href="https://github.com/peoplehum/template-Labelling-Topics-with-wikipedia/issues";>Github > issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> ><th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO >Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> ><td>Apache Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> ><td>already compatible</td> </tr> </table> <br> <h3><a >href="https://github.com/apache/incubator-predictio nio-template-text-classifier">Text Classification</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=apache&repo=incubator-predictionio-template-text-classifier&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Use this engine for general text classification purposes. Uses OpenNLP library for text vectorization, includes t.f.-i.d.f.-based feature transformation and reduction, and uses Spark MLLib's Multinomial Naive Bayes implementation for classification. </p> <p>Support: <a href="https://github.com/apache/incubator-predictionio-template-text-classifier/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://githu b.com/thomasste/template-scala-parallel-dl4j-rntn">Deeplearning4j RNTN</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=thomasste&repo=template-scala-parallel-dl4j-rntn&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Recursive Neural Tensor Network algorithm is supervised learning algorithm used to predict sentiment of sentences. This template is based on deeplearning4j RNTN example: https://github.com/SkymindIO/deeplearning4j-nlp-examples/tree/master/src/main/java/org/deeplearning4j/rottentomatoes/rntn. It's goal is to show how to integrate deeplearning4j library with PredictionIO. </p> <p>Support: <a href="https://github.com/thomasste/template-scala-parallel-dl4j-rntn/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/peoplehum/BagOfWords_SentimentAnalysis_Template";>Sentiment Analysis - Bag of Words Model</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=peoplehum&repo=BagOfWords_SentimentAnalysis_Template&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This sentiment analysis template uses a bag of words model. Given text, the engine will return sentiment as 1.0 (positive) or 0.0 (negative) along with scores indicating how +ve or -ve it is. </p> <p>Support: <a href="https://github.com/peoplehum/BagOfWords_SentimentAnalysis_Template/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable </td> <td>0.10.0-incubating</td> <td>already compatible</td> </tr> </table> <br> <h3><a href="https://github.com/infoquestsolutions/OpenNLP-SentimentAnalysis-Template";>OpenNLP Sentiment Analysis Template</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=infoquestsolutions&repo=OpenNLP-SentimentAnalysis-Template&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Given a sentence, this engine will return a score between 0 and 4. This is the sentiment of the sentence. The lower the number the more negative the sentence is. It uses the OpenNLP library. </p> <p>Support: <a href="https://github.com/infoquestsolutions/OpenNLP-SentimentAnalysis-Template/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>beta</td > <td>0.10.0-incubating</td> <td>already compatible</td> </tr> </table> <br> > <h3><a > href="https://github.com/pawel-n/template-scala-cml-sentiment";>Sentiment > analysis</a></h3> <iframe > src="https://ghbtns.com/github-btn.html?user=pawel-n&repo=template-scala-cml-sentiment&type=star&count=true"; > frameborder="0" align="middle" scrolling="0" width="170px" > height="20px"></iframe> <p> This template implements various algorithms for > sentiment analysis, most based on recursive neural networks (RNN) and > recursive neural tensor networks (RNTN)[1]. It uses an experimental library > called Composable Machine Learning (CML) and the Stanford Parser. The > example data set is the Stanford Sentiment Treebank. </p> <p>Support: <a > href="https://github.com/pawel-n/template-scala-cml-sentiment/issues";>Github > issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> > <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO > Convesion Required</th> </tr> <tr> <td>Parallel</td> < td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/pawel-n/template-scala-parallel-word2vec";>Word2Vec</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=pawel-n&repo=template-scala-parallel-word2vec&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This template integrates the Word2Vec implementation from deeplearning4j with PredictionIO. The Word2Vec algorithm takes a corpus of text and computes a vector representation for each word. These representations can be subsequently used in many natural language processing applications. </p> <p>Support: <a href="https://github.com/pawel-n/template-scala-parallel-word2vec/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td >Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> ><td>0.9.0</td> <td>requires conversion</td> </tr> </table> <br> <h3><a >href="https://github.com/thomasste/template-scala-spark-dl4j-word2vec";>Spark >Deeplearning4j Word2Vec</a></h3> <iframe >src="https://ghbtns.com/github-btn.html?user=thomasste&repo=template-scala-spark-dl4j-word2vec&type=star&count=true"; > frameborder="0" align="middle" scrolling="0" width="170px" >height="20px"></iframe> <p> This template shows how to integrate >Deeplearnign4j spark api with PredictionIO on example of app which uses >Word2Vec algorithm to predict nearest words. </p> <p>Support: <a >href="https://github.com/thomasste/template-scala-spark-dl4j-word2vec/issues";>Github > issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> ><th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO >Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> ><td>Apache Licence 2.0</td> <td>stable</td> <td>0.9.2< /td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/whhone/template-sentiment-analysis";>Sentiment Analysis Template</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=whhone&repo=template-sentiment-analysis&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Given a sentence, return a score between 0 and 4, indicating the sentence's sentiment. 0 being very negative, 4 being very positive, 2 being neutral. The engine uses the stanford CoreNLP library and the Scala binding `gangeli/CoreNLP-Scala` for parsing. </p> <p>Support: <a href="https://github.com/whhone/template-sentiment-analysis/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>None</td> <td>stable</td> <td>0.9.0</td> <td>requires conversion< /td> </tr> </table> <br> <h3><a href="https://github.com/thomasste/template-scala-rnn";>Recursive Neural Networks (Sentiment Analysis)</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=thomasste&repo=template-scala-rnn&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Predicting sentiment of phrases with use of Recursive Neural Network algorithm and OpenNLP parser. </p> <p>Support: <a href="https://github.com/thomasste/template-scala-rnn/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/Ling-Ling/CoreNLP-Text-Classification";>CoreNLP Text Classification</a></h3> <iframe src="https://ghbtns.com/gith ub-btn.html?user=Ling-Ling&repo=CoreNLP-Text-Classification&type=star&count=true" frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This engine uses CoreNLP to do text analysis in order to classify the category a strings of text falls under. </p> <p>Support: <a href="https://github.com/Ling-Ling/CoreNLP-Text-Classification/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>-</td> <td>requires conversion</td> </tr> </table> <br> </div> <div data-tab="Clustering" id="tab-656ff594-05e8-4798-beb7-c8afb9b37ebd"> <h3><a href="https://github.com/sahiliitm/predictionio-MLlibKMeansClusteringTemplate";>MLlibKMeansClustering</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=sahiliitm&repo=predictionio-MLlibKMeansClust eringTemplate&type=star&count=true" frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This is a template which demonstrates the use of K-Means clustering algorithm which can be deployed on a spark-cluster using prediction.io. </p> <p>Support: <a href="https://github.com/sahiliitm/predictionio-MLlibKMeansClusteringTemplate/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>-</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/EmergentOrder/template-scala-topic-model-LDA";>Topc Model (LDA)</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=EmergentOrder&repo=template-scala-topic-model-LDA&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" heig ht="20px"></iframe> <p> A PredictionIO engine template using Latent Dirichlet Allocation to learn a topic model from raw text </p> <p>Support: <a href="https://github.com/EmergentOrder/template-scala-topic-model-LDA/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.4</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/singsanj/KMeans-parallel-template";>KMeans-Clustering-Template</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=singsanj&repo=KMeans-parallel-template&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> forked from PredictionIO/template-scala-parallel-vanilla. It implements the KMeans Algorithm. Can be extended to mainstream implementatio n with minor changes. </p> <p>Support: <a href="https://github.com/singsanj/KMeans-parallel-template/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/peoplehum/template-Labelling-Topics-with-wikipedia";>Topic Labelling with Wikipedia</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=peoplehum&repo=template-Labelling-Topics-with-wikipedia&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This template will label topics (e.g. topic generated through LDA topic modeling) with relevant category by referring to Wikipedia as a knowledge base. </p> <p>Support: <a href="https://github.com/peoplehum/templat e-Labelling-Topics-with-wikipedia/issues">Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> <td>already compatible</td> </tr> </table> <br> </div> <div data-tab="Similarity" id="tab-a5566745-e45e-464c-9acb-f8ac2d5330db"> <h3><a href="https://github.com/alexice/template-scala-parallel-svd-item-similarity";>Content Based SVD Item Similarity Engine</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=alexice&repo=template-scala-parallel-svd-item-similarity&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Template to calculate similarity between items based on their attributesâsometimes called content-based similarity. Attributes can be either numeric or categorical in the las t case it will be encoded using one-hot encoder. Algorithm uses SVD in order to reduce data dimensionality. Cosine similarity is now implemented but can be easily extended to other similarity measures. </p> <p>Support: <a href="https://groups.google.com/forum/#!forum/actionml-user";>The Universal Recommender user group</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/goliasz/pio-template-text-similarity";>Cstablo-template-text-similarity-classification</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=goliasz&repo=pio-template-text-similarity&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Text similarity engine based on Word2Vec algorithm. Builds vectors of full documents in training phase. Finds similar documents in query phase. </p> <p>Support: <a href="https://github.com/goliasz/pio-template-text-similarity/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/ramaboo/template-scala-parallel-similarproduct-with-rating";>Similar Product with Rating</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=ramaboo&repo=template-scala-parallel-similarproduct-with-rating&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Similar product template with rating support! Used for the MovieLens Demo. </p> <p>Support: <a href="htt ps://github.com/ramaboo/template-scala-parallel-similarproduct-with-rating/issues">Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>beta</td> <td>0.9.0</td> <td>requires conversion</td> </tr> </table> <br> </div> <div data-tab="Other" id="tab-14574064-995c-430c-bacc-9e4cc128ecc3"> <h3><a href="https://github.com/goliasz/pio-template-fpm";>Frequent Pattern Mining</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=goliasz&repo=pio-template-fpm&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Template uses FP Growth algorithm allowing to mine for frequent patterns. Template returns subsequent items together with confidence score. </p> <p>Support: <a href="https://github.com/goliasz/pio-template-fpm/issues";>Github issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/anthill/template-decision-tree-feature-importance";>template-decision-tree-feature-importance</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=anthill&repo=template-decision-tree-feature-importance&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This template shows how to use spark' decision tree. It enables : - both categorical and continuous features - feature importance calculation - tree output in json - reading training data from a csv file </p> <p>Support: <a href="https://github.com/anthill/template-decision-tree-feature-importance/issues";>Github issues </a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.9.0</td> <td>requires conversion</td> </tr> </table> <br> <h3><a href="https://github.com/apache/incubator-predictionio-template-skeleton";>Skeleton</a></h3> <iframe src="https://ghbtns.com/github-btn.html?user=apache&repo=incubator-predictionio-template-skeleton&type=star&count=true"; frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Skeleton template is for developing new engine when you find other engine templates do not fit your needs. This template provides a skeleton to kick start new engine development. </p> <p>Support: <a href="http://predictionio.incubator.apache.org/support/";>Apache PredictionIO mailing lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th > <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion > Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence > 2.0</td> <td>stable</td> <td>0.9.2</td> <td>already compatible</td> </tr> > </table> <br> </div> </div> </div></div></div></div><footer><div > class="container"><div class="seperator"></div><div class="row"><div > class="col-md-6 footer-link-column"><div > class="footer-link-column-row"><h4>Community</h4><ul><li><a > href="//predictionio.incubator.apache.org/install/" > target="blank">Download</a></li><li><a > href="//predictionio.incubator.apache.org/" > target="blank">Docs</a></li><li><a > href="//github.com/apache/incubator-predictionio" > target="blank">GitHub</a></li><li><a > href="mailto:[email protected]"; > target="blank">Subscribe to User Mailing List</a></li><li><a > href="//stackoverflow.com/questions/tagged/predictionio" > target="blank">Stackoverflow</a></li></ul></div></div><div class="col-md-6 > footer-link-column"><div class ="footer-link-column-row"><h4>Contribute</h4><ul><li><a href="//predictionio.incubator.apache.org/community/contribute-code/" target="blank">Contribute</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">Source Code</a></li><li><a href="//issues.apache.org/jira/browse/PIO" target="blank">Bug Tracker</a></li><li><a href="mailto:[email protected]"; target="blank">Subscribe to Development Mailing List</a></li></ul></div></div></div><div class="row"><div class="col-md-12 footer-link-column"><a class="pull-right" href="http://incubator.apache.org/projects/predictionio.html";><img alt="Apache Incubator" src="/images/logos/apache_incubator-6954bd16.png"/></a><span>Apache PredictionIO is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. 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