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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis Snowflake Quickstart covers the basics of training machine learning models, interpreting them, and deploying them to make predictions.\u003C/p\u003E\n","\u003Cp\u003EWith Dataiku’s Visual Snowpark ML plugin - you won’t need to write a single line of code. That’s right!\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDomain experts - you don’t need to fuss around with learning programming to inject ML into your team.\u003C/li\u003E\u003Cli\u003EData Scientists - we’re all trying to roll our own abstracted ML training and deployment platform - and we think you’ll like this one.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EUse Case\u003C/h3\u003E\n","\u003Cp\u003EConsumer lending is difficult. What factors about an individual and their loan application could indicate whether they’re likely to pay back the loan? How can our bank optimize the loans approved and rejected based on our risk tolerance?\nWe’ll use machine learning to help with this decision making process. Our model will learn patterns between historical loan applications and default, then we can use it to make predictions for a fresh batch of applications.\u003C/p\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n","\u003Cp\u003EThe exercises in this lab will walk you through the steps to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUse Snowflake's "Partner Connect" to create a Dataiku cloud trial\u003C/li\u003E\u003Cli\u003ECreate a Snowpark-optimized warehouse (for ML workloads)\u003C/li\u003E\u003Cli\u003EUpload a base project in Dataiku with our data sources in Snowflake\u003C/li\u003E\u003Cli\u003ELook at our loan data, understand trends through correlation matrices\u003C/li\u003E\u003Cli\u003ETrain, interpret, and deploy Machine Learning models in Dataiku - powered by Snowpark ML\u003C/li\u003E\u003Cli\u003EUse our trained model to make new predictions\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E(Optional)\u003C/code\u003E Set up an MLOps process to retrain the model, check for accuracy, and make new predictions on a weekly basis\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake free 30-day trial environment\u003C/li\u003E\u003Cli\u003EDataiku free 14-day trial environment (via Snowflake Partner Connect)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Your Snowflake Lab Environment\u003C/h2\u003E\n","\u003Cp\u003EIf you haven't already, \u003Ca href=\"https://trial.snowflake.com/\"\u003Eregister for a Snowflake free 30-day trial\u003C/a\u003E The rest of the sections in this lab assume you are using a new Snowflake account created by registering for a trial.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Please ensure that you use the \u003Cstrong\u003Esame email address\u003C/strong\u003E for both your Snowflake and Dataiku sign up\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERegion\u003C/strong\u003E  - Although not a requirement we'd suggest you select the region that is physically closest to you for this lab\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECloud Provider\u003C/strong\u003E  -  Although not a requirement we'd suggest you select \u003Ccode\u003EAWS\u003C/code\u003E for this lab\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake edition\u003C/strong\u003E  -  We suggest you select select the \u003Ccode\u003EEnterprise edition\u003C/code\u003E so you can leverage some advanced capabilities that are not available in the Standard Edition.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAfter activation, you will create a \u003Ccode\u003Eusername\u003C/code\u003Eand \u003Ccode\u003Epassword\u003C/code\u003E. Write down these credentials. \u003Cstrong\u003EBookmark this URL for easy, future access\u003C/strong\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAbout the screen captures, sample code, and environment:\u003C/strong\u003E <br> Screen captures in this lab depict examples and results that may slightly vary from what you may see when you complete the exercises.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Your Dataiku Lab Environment (Via Snowflake Partner Connect)\u003C/h2\u003E\n","\u003Ch3\u003ELog Into the Snowflake User Interface (UI)\u003C/h3\u003E\n","\u003Cp\u003EOpen a browser window and enter the URL of your Snowflake 30-day trial environment. You should see the login screen below. Enter your unique credentials to log in.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E<br>\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003EYou may see "welcome" and "helper" boxes in the UI when you log in for the first time. Close them by clicking on Skip for now in the bottom right corner in the screenshot below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC2.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate Dataiku trial via Partner Connect\u003C/h3\u003E\n","\u003Cp\u003EAt the top right of the page, confirm that your current role is \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E, by clicking on your profile on the top right.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on \u003Ccode\u003EData Products\u003C/code\u003E on the left-hand menu\u003C/li\u003E\u003Cli\u003EClick on \u003Ccode\u003EPartner Connect\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESearch for Dataiku\u003C/li\u003E\u003Cli\u003EClick on the \u003Ccode\u003EDataiku\u003C/code\u003E tile\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC3.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EDepending on which screen you are on you may not see the full menu as above but hovering over\nthe Data Products (Cloud) icon will show the options\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThis will automatically create the connection parameters required for Dataiku to connect to Snowflake. Snowflake will create a dedicated database, warehouse, system user, system password and system role, with the intention of those being used by the Dataiku account.\u003C/p\u003E\n","\u003Cp\u003EFor this lab we’d like to use the \u003Cstrong\u003EPC_DATAIKU_USER\u003C/strong\u003E to connect from Dataiku to Snowflake, and use the \u003Cstrong\u003EPC_DATAIKU_WH\u003C/strong\u003E when performing activities within Dataiku that are pushed down into Snowflake.\u003C/p\u003E\n","\u003Cp\u003EThis is to show that a Data Science team working on Dataiku and by extension on Snowflake can work completely independently from the Data Engineering team that works on loading data into Snowflake using different roles and warehouses.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC4.png\" alt=\"img\"\u003E\n<br>\n<br>\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Ccode\u003EConnect\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EYou will get a pop-ip which tells you your partner account has been created. Click on \u003Ccode\u003EActivate\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EInformational Note:\u003C/strong\u003E <br> If you are using a different Snowflake account than the one created\nat the start, you may get a screen asking for your email details. Click on ‘Go to Preferences’ and\npopulate with your email details\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThis will launch a new page that will redirect you to a launch page from Dataiku.\u003C/p\u003E\n","\u003Cp\u003EHere, you will have two options:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELogin with an existing Dataiku username\u003C/li\u003E\u003Cli\u003ESign up for a new Dataiku account\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EWe assume that you’re new to Dataiku, so ensure the \u003Ccode\u003E“Sign Up” box is selected\u003C/code\u003E, and sign up with either GitHub, Google or your email address and your new password.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMake sure you use the same email address that you used for your Snowflake trial\u003C/strong\u003E.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EClick sign up\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC5.png\" alt=\"img\"\u003E\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003EWhen using your email address, ensure your password fits the following criteria:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EAt least 8 characters in length\u003C/li\u003E\u003Cli\u003EShould contain:\nLower case letters (a-z)\nUpper case letters (A-Z)\nNumbers (i.e. 0-9)\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou should have received an email from Dataiku to the email you have signed up with. Activate your Dataiku account via the email sent.\u003C/p\u003E\n","\u003Ch3\u003EReview Dataiku Setup\u003C/h3\u003E\n","\u003Cp\u003EUpon clicking on the activation link, please briefly review the Terms of Service of Dataiku Cloud. In order to do so, please scroll down to the bottom of the page.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on \u003Ccode\u003EI AGREE\u003C/code\u003E and then click on \u003Ccode\u003ENEXT\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC6.png\" alt=\"img\"\u003E\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EComplete your sign up some information about yourself and then click on \u003Ccode\u003EStart\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou will be redirected to the Dataiku Cloud Launchpad site. Click \u003Ccode\u003EGOT IT!\u003C/code\u003E to continue.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC7.png\" alt=\"img\"\u003E\n<br>\n<br>\nThis is the Cloud administration console where you can perform tasks such as inviting other users to collaborate, add plugin extensions, install industry solutions to accelerate projects as well as access community and academy resources to help your learning journey.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E It may take several minutes for your instance to Dataiku to start up the first time,\nduring this time you will not be able to add the extension as described below.\nYou can always come back to this task later if time doesn't allow now\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EAdd the Visual SnowparkML Plugin\u003C/h3\u003E\n","\u003Cp\u003EIt's beyond the scope of this course to cover plugins in depth but for this lab we would like to enable a few the Visual SnowparkML plugin so lets do that now.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on \u003Ccode\u003EPlugins\u003C/code\u003E on the left menu\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003E+ ADD A PLUGIN\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EFind \u003Ccode\u003EVisual SnowparkML\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECheck \u003Ccode\u003EInstall on my Dataiku instance\u003C/code\u003E, and click \u003Ccode\u003EINSTALL\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC8.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC9.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on \u003Ccode\u003ECode Envs\u003C/code\u003E on the left menu\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003EADD A CODE ENVIRONMENT\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect  \u003Ccode\u003ENEW PYTHON ENV\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EName your code env \u003Ccode\u003Epy_39_snowpark\u003C/code\u003E \u003Cstrong\u003ENOTE: The name must match exactly\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ECREATE\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC10.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC11.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC12.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003ESelect `Pandas 1.3 (Python 3.7 and above) from Core Packages menu\u003C/li\u003E\u003Cli\u003EAdd the following packages\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Escikit-learn==1.3.2\nmlflow==2.9.2\nstatsmodels==0.12.2\nprotobuf==3.16.0\nxgboost==1.7.3\nlightgbm==3.3.5\nmatplotlib==3.7.1\nscipy==1.10.1\nsnowflake-snowpark-python==1.14.0\nsnowflake-snowpark-python[pandas]==1.14.0\nsnowflake-connector-python[pandas]==3.7.0\nMarkupSafe==2.0.1\ncloudpickle==2.0.0\nflask==1.0.4\nJinja2==2.11.3\nsnowflake-ml-python==1.5.0\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"8\"\u003E\u003Cli\u003ESelect \u003Ccode\u003Erebuild env\u003C/code\u003E from the menu on the left\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ESave and update\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC13.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou've now successfully set up your Dataiku trial account via Snowflake's Partner Connect. We are now ready to continue with the lab. For this, move back to your Snowflake browser.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate a Snowpark Optimized Warehouse in Snowflake\u003C/h2\u003E\n","\u003Ch3\u003EReturn to the Snowflake UI\u003C/h3\u003E\n","\u003Cp\u003EWe will now create an optimized warehouse\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Ccode\u003EAdmin\u003C/code\u003E from the bottom of the left hand menu\u003C/li\u003E\u003Cli\u003EThen \u003Ccode\u003EWarehouses\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThen click \u003Ccode\u003E+ Warehouse\u003C/code\u003E in the top right corner\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce in the \u003Ccode\u003ENew Warehouse\u003C/code\u003E creation screen perform the following steps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate a new warehouse called \u003Ccode\u003ESNOWPARK_WAREHOUSE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EFor the type select \u003Ccode\u003ESnowpark-optimized\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003EMedium\u003C/code\u003E as the size\u003C/li\u003E\u003Cli\u003ELastly click \u003Ccode\u003ECreate Warehouse\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH2.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect your new Snowflake Warehouse by \u003Ccode\u003Eclicking on it once\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH3.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe need to permission the Dataiku Role that was created by Partner Connect in the earlier chapter for this new warehouse.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EScroll down to Privileges, and click \u003Ccode\u003E+ Privilege\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH4.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EFor the Role select the role \u003Ccode\u003EPC_DATAIKU_ROLE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EUnder Pivileges grant the \u003Ccode\u003EUSAGE\u003C/code\u003E privilege\u003C/li\u003E\u003Cli\u003EClick on \u003Ccode\u003EGrant Privileges\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH5.png\" alt=\"img\"\u003E\n<br>\n<br>\nYou should now see your new privileges have been applied\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH6.png\" alt=\"img\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EImport a Baseline Dataiku Project\u003C/h2\u003E\n","\u003Cp\u003EReturn to the Dataiku trial launchpad in your browser\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EEnsure you are on the \u003Ccode\u003EOverview\u003C/code\u003E page\u003C/li\u003E\u003Cli\u003EClick on \u003Ccode\u003EOPEN INSTANCE\u003C/code\u003E to get started.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/IMP1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECongratulations you are now using the Dataiku platform! For the remainder of this lab we will be working from this environment which is called the design node, its the pre-production environment where teams collaborate to build data products.\u003C/p\u003E\n","\u003Cp\u003ENow lets import our first project.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDownload the project zip file to your computer - \u003Cstrong\u003EDon’t unzip it!\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://dataiku-partnerships.s3.eu-west-1.amazonaws.com/LOANDEFAULTSSNOWPARKML.zip\"\u003EStarter Project\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EOnce you have download the starter project we can create our first project\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Ccode\u003E+ NEW PROJECT\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThen \u003Ccode\u003EImport project\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/IMP2.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EChoose the .zip file you just downloaded, then click \u003Ccode\u003EIMPORT\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/IMP3.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003EYou should see a project with 4 dataset - two local CSVs which we’ve then imported into Snowflake\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/IMP4.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow that we have all our setup done, lets start working with our data.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAnalyze trends in the data\u003C/h2\u003E\n","\u003Cp\u003EBefore we begin analyzing the data in our new project lets take a minute to understand some of the concepts and terminology of a project in Dataiku.\u003C/p\u003E\n","\u003Cp\u003EHere is the project we are going to build along with some annotations to help you understand some key concepts in Dataiku.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends0.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EA \u003Cstrong\u003Edataset\u003C/strong\u003E is represented by a blue square with a symbol that depicts the dataset type or connection. The initial datasets (also known as input datasets) are found on the left of the Flow. In this project, the input dataset will be the one we created in the first part of the lab.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EA \u003Cstrong\u003Erecipe\u003C/strong\u003E in Dataiku DSS (represented by a circle icon with a symbol that depicts its function) can be either visual or code-based, and it contains the processing logic for transforming datasets. In addition to the core Visual and Code recipes Dataiku can be expanded with the use of plugins which are either from the freely available Dataiku library or developed by users. We will use the Visual SnowparkML plugin today\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMachine learning processes\u003C/strong\u003E are represented by green icons.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EActions Menu\u003C/strong\u003E is shown on the right pane and is context sensitive.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWhatever screen you are currently in you can always return to the main \u003Cstrong\u003EFlow\u003C/strong\u003E by clicking the \u003Cstrong\u003EFlow\u003C/strong\u003E symbol from the top menu (also clicking the project name will take you back to the main Project page).\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EYou can refer back to this completed project screenshot if you want to check your progress through the lab.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EDouble click\u003C/code\u003E into the \u003Ccode\u003ELOAN_REQUESTS_KNOWN_SF\u003C/code\u003E dataset. This is our dataset of historical loan applications, a number of attributes about them, and whether the loan was paid back or defaulted (the DEFAULTED column - 1.0 = default, 0.0 = paid back).\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends1.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick the \u003Ccode\u003EStatistics\u003C/code\u003E tab on the top\u003C/li\u003E\u003Cli\u003ENext click \u003Ccode\u003E+ Create first worksheet\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends2.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThen select \u003Ccode\u003EAutomatically suggest analyses\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends3.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EChoose a few of the suggestions, be sure to include the Correlation Matrix in your selections then click \u003Ccode\u003ECREATE SELECTED CARDS\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends4.png\" alt=\"img\"\u003E\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuestion:\u003C/strong\u003E What trends do you notice in the data?\u003C/p\u003E\n","\u003Cp\u003ELook at the correlation matrix, and the DEFAULTED row. Notice that INTEREST_RATE has the highest correlation with DEFAULTED. We should definitely include this feature in our models!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends5.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPositive\u003C/strong\u003E correlation means as INTEREST_RATE rises, so does DEFAULTED (higher interest rate -> higher probability of default).\nNotice MONTHLY_INCOME has a \u003Cstrong\u003Enegative\u003C/strong\u003E correlation to DEBT_TO_INCOME_RATIO. This means that as monthly income goes up, applicants’ debt to income ratio generally goes down.\u003C/p\u003E\n","\u003Cp\u003ESee if you can identify a few other features we should include in our models.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETrain Machine Learning Models\u003C/h2\u003E\n","\u003Ch3\u003ECreate a new Visual SnowparkML recipe\u003C/h3\u003E\n","\u003Cp\u003ENow we will tran an ML model using our plugin. Return to the flow either by clicking on the flow icon or by using the keyboard shortcut \u003Ccode\u003E(g+f)\u003C/code\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EFrom the Flow click once on the \u003Ccode\u003ELOAN_REQUESTS_KNOWN_SF\u003C/code\u003E dataset.\u003C/li\u003E\u003Cli\u003EFrom the \u003Ccode\u003EActions menu\u003C/code\u003E on the right scroll down and select the \u003Ccode\u003EVisual Snowpark ML plugin\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML2.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick Train ML Models on Snowpark\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML3.png\" alt=\"img\"\u003E\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003EWe now need to set our three \u003Ccode\u003EOutputs\u003C/code\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on \u003Ccode\u003ESet\u003C/code\u003E under the  \u003Ccode\u003EGenerated Train Dataset\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML4.png\" alt=\"img\"\u003E\n<br>\n<br>\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESet the name to \u003Ccode\u003Etrain\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003EPC_DATAIKU_DB\u003C/code\u003E to store into\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ECREATE DATASET\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML5.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe will now repeat this process for the other two outputs\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on \u003Ccode\u003ESet\u003C/code\u003E under the  \u003Ccode\u003EGenerated Test Dataset\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Col\u003E\u003Cli\u003ESet the name to \u003Ccode\u003Etest\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003EPC_DATAIKU_DB\u003C/code\u003E to store into\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ECREATE DATASET\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML6.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on \u003Ccode\u003ESet\u003C/code\u003E under the  \u003Ccode\u003EModels Folder\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Col\u003E\u003Cli\u003ESet the name to \u003Ccode\u003Emodels\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003Edataiku-managed-storage\u003C/code\u003E to store into\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ECREATE FOLDER\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML7.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EYour three outputs should now look like the image below. Finally click on \u003Ccode\u003ECREATE\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML8.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E<br>\n<br>\n<br>\u003C/p\u003E\n","\u003Ch3\u003EDefine model training settings\u003C/h3\u003E\n","\u003Cp\u003ELets fill out the parameters for our training session.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EGive your model a name\u003C/li\u003E\u003Cli\u003EChoose \u003Ccode\u003EDEFAULTED\u003C/code\u003E as the target column\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003ETwo-class classification\u003C/code\u003E as the prediction type\u003C/li\u003E\u003Cli\u003EChoose \u003Ccode\u003EROC AUC\u003C/code\u003E as our model metric. This is a common machine learning metric for classification problems.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ELeave the Train ratio and random seed as is. This will split our input dataset into 80% of records for training, leaving 20% for an unbiased evaluation of the model\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML9.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EChoose the following features to include in our model. Make sure to make a selection for Encoding / Rescaling and Impute Missing Values With - \u003Cstrong\u003Edon’t leave them empty\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML13.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML14.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EChoose the following algorithms to start. We’ll go through the basics of these algorithms after we kick off our model training.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML15.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELeave the Search space limit as 4\u003C/li\u003E\u003Cli\u003EWrite SNOWPARK_WAREHOUSE to use the Snowpark-optimized warehouse we created earlier.\u003C/li\u003E\u003Cli\u003ECheck the “Deploy to Snowflake ML Model Registry” box. This will deploy our best trained model to Snowflake’s Model Registry - where we can use it to make predictions later on.\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EFinally Click the \u003Ccode\u003ERUN\u003C/code\u003E button in the bottom left hand corner to start training our models.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML16.png\" alt=\"img\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMachine Learning - Basic Theory (Optional)\u003C/h2\u003E\n","\u003Cp\u003EWhile we’re waiting for our models to train, let’s learn a bit about machine learning. This is an oversimplification of some complicated topics. If you’re interested there are links at the end of the course for the Dataiku Academy and many other free resources online.\u003C/p\u003E\n","\u003Ch3\u003EMachine Learning, Classification, and Regression\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMachine learning\u003C/strong\u003E - the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOversimplified definition of machine learning\u003C/strong\u003E - Fancy pattern matching based on the data you feed into it\u003C/p\u003E\n","\u003Cp\u003EThe two most common types of machine learning solutions are supervised and unsupervised learning.\n<br>\n\u003Cstrong\u003ESupervised learning\u003C/strong\u003E\nGoal: predict a target variable\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Ecategory = classification\u003C/li\u003E\u003Cli\u003Enumerical value = regression\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EExamples:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EPredict the sales price of an apartment (regression)\u003C/li\u003E\u003Cli\u003EForecast the winner of an election (classification)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUnsupervised learning\u003C/strong\u003E\nGoal: identify patterns\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EGroup similar individuals  = \u003Cstrong\u003Eclustering\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EFind anomalies = \u003Cstrong\u003Eanomaly detection\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EExamples:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESegment consumers according to their behavior (clustering)\u003C/li\u003E\u003Cli\u003EFind anomalous opioid shipments from a DEA database (anomaly detection)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E<br>\n<br>\nOur problem - predicting loan defaults, is a **supervised, classification problem**.\u003C/p\u003E\n","\u003Cp\u003EWe need a structured dataset to train a model, in particular:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERows measuring \u003Cstrong\u003Eindividual observations\u003C/strong\u003E (one transaction per row)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETarget column\u003C/strong\u003E with real labels of what we want to predict\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOther columns\u003C/strong\u003E (features) that the model can use to predict the target (through fancy pattern matching)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB1.png\" alt=\"img\"\u003E\n<br>\u003C/p\u003E\n","\u003Ch3\u003ETrain / Test split\u003C/h3\u003E\n","\u003Cp\u003EOnce we have a structured dataset with observations, a target, and features, we split it into train and test sets\u003C/p\u003E\n","\u003Cp\u003EWe could split it:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERandomly\u003C/li\u003E\u003Cli\u003EBased on time\u003C/li\u003E\u003Cli\u003EOther criteria\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EA random split of 80% train / 20% test is common\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB2.png\" alt=\"img\"\u003E\n<br>\u003C/p\u003E\n","\u003Cp\u003EWe train models on the train set, then evaluate them on the test set. This way, we can simulate how the model will perform on data that it hasn’t seen before.\u003C/p\u003E\n","\u003Ch3\u003EFeature Handling\u003C/h3\u003E\n","\u003Cp\u003ETwo keys to choosing features to include in our model:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOnly include variables that you know you’ll have at the time you need to make predictions (e.g. at time of sale for future credit card transactions)\u003C/li\u003E\u003Cli\u003EUse your domain knowledge - if you think a variable could be a driver of the target, include it in your model!\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EMost machine learning models require variables to be a specific format to be able to find patterns in the data.\u003C/p\u003E\n","\u003Cp\u003EWe can generally break up our variables into two categories:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ENumeric\u003C/strong\u003E - e.g. AMOUNT_REQUESTED, DEBT_TO_INCOME_RATIO\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECategorical\u003C/strong\u003E - e.g. LOAN_PURPOSE, STATE\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EHere are some ways to transform these types of features:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENumeric\u003C/strong\u003E - e.g. AMOUNT_REQUESTED, DEBT_TO_INCOME_RATIO\n<br>\u003C/p\u003E\n","\u003Cp\u003EThings you typically want to consider:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EImpute\u003C/strong\u003E a number for rows missing values. Average, median are common\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERescale\u003C/strong\u003E the variable. Standard rescaling is common (this transforms a value to its Z-score)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB3.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECategorical\u003C/strong\u003E - e.g. LOAN_PURPOSE, STATE\n<br>\u003C/p\u003E\n","\u003Cp\u003EThings you typically want to consider:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EEncode\u003C/strong\u003E values with a number. Dummy encoding, ordinal encoding are common\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EImpute\u003C/strong\u003E a value for rows missing values. You can treat a missing value as its own category, or impute with the most common value.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB4.png\" alt=\"img\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E<br>\u003C/p\u003E\n","\u003Ch3\u003EMachine Learning Algorithms\u003C/h3\u003E\n","\u003Cp\u003E<br>\u003C/p\u003E\n","\u003Cp\u003ELet’s go through a few common machine learning algorithms.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELinear Regression\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EFor linear regression (predicting a number), we find the line of best fit, plotting our feature variables and our target\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Ey = b0 + b1 * x\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EIf we were training a model to predict exam scores based on # hours of study, we would solve for this equation\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Eexam_score = b0 + b1 * (num_hours_study)\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB5.png\" alt=\"img\"\u003E\n<br>\u003C/p\u003E\n","\u003Cp\u003EWe use math (specifically a technique called Ordinary Least Squares[1]) to find the b0 and b1 of our best fit line\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Eexam_score = b0 + b1 * (num_hours_study)\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Eexam_score = 32 + 8 * (num_hours_study)\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB6.png\" alt=\"img\"\u003E\n<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELogistic Regression\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ELogistic regression is similar to linear regression - except built for a classification problem (e.g. loan default prediction).\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Elog(p/1-p) = b0 + b1 * (num_hours_study)\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Elog(p/1-p) = 32 + 8 * (num_hours_study)\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Ep = probability of exam success\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB7.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDecision Trees\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EImagine our exam pass/fail model with more variables.\u003C/p\u003E\n","\u003Cp\u003EDecision trees will smartly create if / then statements, sending each row along a branch until it makes a prediction of your target variable\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB8.png\" alt=\"img\"\u003E\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERandom Forest\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EA Random Forest model trains many decision trees, introduces randomness into each one, so they behave differently, then averages their predictions for a final prediction\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB9.png\" alt=\"img\"\u003E\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOverfitting\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWe want our ML model to be able to understand true patterns in the data - uncover the signal, and ignore the noise (random, unexplained variation in the data)\u003C/p\u003E\n","\u003Cp\u003EOverfitting is an undesirable machine learning behavior that occurs when the machine learning model gives accurate predictions for training data but not for new data\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB10.png\" alt=\"img\"\u003E\n<br>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB11.png\" alt=\"img\"\u003E\n<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to control for overfitting\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ELogistic Regression\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIncreasing C shrinks your equation coefficients\u003C/li\u003E\u003Cli\u003EIncrease C to control more for overfitting\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EExample\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003EC = 0.01: log(p/1-p) = 32 + 8 * (num_hours_study) + 6 * (num_hours_sleep)\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003EC = 0.1: log(p/1-p) = 32 + 5 * (num_hours_study) + 4 * (num_hours_sleep)\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003EC = 1: log(p/1-p) = 32 + 3 * (num_hours_study) + 2 * (num_hours_sleep)\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003EC = 10: log(p/1-p) = 32 + 2 * (num_hours_study) + 0 * (num_hours_sleep)\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003EC = 100: log(p/1-p) = 32 + 1 * (num_hours_study) + 0 * (num_hours_sleep)\u003C/code\u003E\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003ERandom Forest\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EMaximum depth of tree\u003C/strong\u003E  how far down can each decision tree go?\u003C/li\u003E\u003Cli\u003EDecrease this to control more for overfitting\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor more in-depth tutorials and self-paced machine learning courses see the links to Dataiku's freely available Academy in the last chapter of this course\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModel Evaluation\u003C/h2\u003E\n","\u003Cp\u003EOnce we’ve trained our models, we’ll want to take a deeper dive deep into how they’re performing, what features they’re considering, and whether they may be biased. Dataiku has a number of tools for evaluating models.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EDouble click\u003C/code\u003E on your model (Green diamond) from the flow\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval1.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EYou’ll see your best trained model here. \u003Ccode\u003EClick\u003C/code\u003E into it.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval2.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESelect \u003Ccode\u003EFeature Importance\u003C/code\u003E from the menu on the left side\u003C/li\u003E\u003Cli\u003EThen click \u003Ccode\u003ECOMPUTE NOW\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval3.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\nHere we can see that the top 3 features impacting the model are applicants’ FICO scores, the interest rate of the loan, and the amount requested. This makes sense!\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval4.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003EScroll down on this page - you’ll see the directional effect of each feature on default predictions. You can see that the higher FICO scores generally mean lower probability of default.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval5.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the \u003Ccode\u003EConfusion matrix\u003C/code\u003E tab from the menu on the left.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EHere we can see how the model would have performed on the hold-out test set of loan applicants. Notice that my model is very good at catching defaulters (83 Predicted 1.0 out of 84 Actually 1.0), at the expense of mistakenly rejecting 124 applicants that would have paid back their loan.\u003C/p\u003E\n","\u003Cp\u003ETry moving the threshold bar back and forth. It will cause the model to be more or less sensitive. Based on your business problem, you may want a higher or lower threshold.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval6.png\" alt=\"img\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMake Predictions (Scoring)\u003C/h2\u003E\n","\u003Cp\u003EUsing a machine learning model to make predictions is called \u003Ccode\u003Escoring\u003C/code\u003E or \u003Ccode\u003Einference\u003C/code\u003E\u003C/p\u003E\n","\u003Ch3\u003EScore the unknown loan applications using the trained model\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EGo to the project Flow, click once on the \u003Ccode\u003ELOAN_REQUESTS_UNKNOWN_SF\u003C/code\u003E dataset\u003C/li\u003E\u003Cli\u003EThen click on the \u003Ccode\u003EVisual Snowpark ML\u003C/code\u003E plugin from the right hand Actions menu.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score1.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick \u003Ccode\u003EScore New Records using Snowpark\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score2.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003EWe need to add our model as an input and set an output dataset for the results of the scoring.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the \u003Ccode\u003EInputs\u003C/code\u003E under the \u003Ccode\u003ESaved Model\u003C/code\u003E option click on \u003Ccode\u003ESET\u003C/code\u003E to add your saved model\u003C/li\u003E\u003Cli\u003EIn the \u003Ccode\u003EOutputs\u003C/code\u003E section under \u003Ccode\u003EScored Dataset Option\u003C/code\u003E click on \u003Ccode\u003ESET\u003C/code\u003E and give your output dataset a name\u003C/li\u003E\u003Cli\u003EFor \u003Ccode\u003EStore into\u003C/code\u003E use the `PC_DATAIKU_DB** connection\u003C/li\u003E\u003Cli\u003EClick on \u003Ccode\u003ECREATE DATASET\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score5.png\" alt=\"img\"\u003E\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EYour screen should now look like this. Go ahead and \u003Ccode\u003Eclick on CREATE\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score6.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EMake sure the warehouse you created earlier \u003Ccode\u003ESNOWPARK_WAREHOUSE\u003C/code\u003E is selected then click on \u003Ccode\u003ERUN\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score7.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\nWhen it finishes, your flow should look like this\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score8.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EDouble click\u003C/code\u003E into the output scored dataset - scroll to the right, and you should see predictions of whether someone is likely to pay back their loan or not!\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score9.png\" alt=\"img\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMLOps (optional)\u003C/h2\u003E\n","\u003Cp\u003ELet’s say we want to automatically run new loan applications through our model every week on Sunday night.\u003C/p\u003E\n","\u003Cp\u003EAssume that \u003Ccode\u003ELOAN_REQUESTS_UNKNOWN\u003C/code\u003E is a live dataset of new loan applications that is updated throughout the week.\u003C/p\u003E\n","\u003Cp\u003EWe want to rerun all the recipes leading up to unknown_loans_scored, where our model makes predictions.\u003C/p\u003E\n","\u003Ch3\u003EBuild a weekly scoring scenario\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClick into the Scenarios tab\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops1.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick \u003Ccode\u003E+ CREATE YOUR FIRST SCENARIO\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops2.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EName your scenario something like \u003Ccode\u003E“Weekly Loan Application Scoring”\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops3.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAdd a time-based trigger\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops4.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESet the trigger to run \u003Ccode\u003Eevery week on Sunday at 9pm\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops5.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn the \u003Ccode\u003ESteps\u003C/code\u003E tab, click \u003Ccode\u003EAdd Step\u003C/code\u003E, then \u003Ccode\u003EBuild / Train\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops6.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAdd a dataset to build\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops7.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThen choose the \u003Ccode\u003Eunknown_loans_scored\u003C/code\u003E dataset\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops8.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECheck the \u003Ccode\u003EForce-build\u003C/code\u003E button to recursively build all datasets leading up to \u003Ccode\u003Eunknown_loans_scored\u003C/code\u003E, then click the \u003Ccode\u003Erun\u003C/code\u003E button to test it out.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops9.png\" alt=\"img\"\u003E\n<br>\n<br>\n<br>\n<br>\u003C/p\u003E\n","\u003Cp\u003EYou’ll be able to see scenario run details in the “Last runs” tab\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops10.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EBuild a monthly model retraining scenario (optional)\u003C/h3\u003E\n","\u003Cp\u003EIt’s good practice to retrain machine learning models on a regular basis with more up-to-date data. The world changes around us; the patterns of loan applicant attributes affecting default probability are likely to change too.\u003C/p\u003E\n","\u003Cp\u003EIf you have time you can assume that LOAN_REQUESTS_KNOWN is a live dataset of historical loan applications that is updated with new loan payback and default data on an ongoing basis.\u003C/p\u003E\n","\u003Cp\u003EYou can automatically retrain your model every month with scenarios, and put in a AUC check to make sure that the model is performing and build the scored dataset\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion & Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations on completing this introductory lab exercise! Congratulations! You've mastered the Snowflake basics and you’ve taken your first steps toward a no-code approach to training machine learning models with Dataiku.\u003C/p\u003E\n","\u003Cp\u003EYou have seen how Dataiku's deep integrations with Snowflake can allow teams with different skill sets get the most out of their data at every stage of the machine learning lifecycle.\u003C/p\u003E\n","\u003Cp\u003EWe encourage you to continue with your free trial and continue to refine your models and by using some of the more advanced capabilities not covered in this lab.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUse Snowflake's "Partner Connect" to create a Dataiku cloud trial\u003C/li\u003E\u003Cli\u003ECreate a Snowpark-optimized warehouse (for ML workloads)\u003C/li\u003E\u003Cli\u003EUpload a base project in Dataiku with our data sources in Snowflake\u003C/li\u003E\u003Cli\u003ELook at our loan data, understand trends through correlation matrices\u003C/li\u003E\u003Cli\u003ETrain, interpret, and deploy Machine Learning models in Dataiku - powered by Snowpark ML\u003C/li\u003E\u003Cli\u003EUse our trained model to make new predictions\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E(Optional)\u003C/code\u003E Set up an MLOps process to retrain the model, check for accuracy, and make new predictions on a weekly basis\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EJoin the \u003Ca href=\"https://community.snowflake.com/s/\"\u003ESnowflake Community\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EJoin the \u003Ca href=\"https://community.dataiku.com/\"\u003EDataiku Community\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ESign up for \u003Ca href=\"http://https://community.snowflake.com/s/snowflake-university\"\u003ESnowflake University\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EJoin the \u003Ca href=\"https://academy.dataiku.com/\"\u003EDataiku Academy\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build ML models without code using Dataiku's visual interface connected to Snowflake for rapid experimentation.","title":"A No Code Approach to Machine Learning with Snowflake and Dataiku","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"title":"Quickstart Article Body","dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nThis Snowflake Quickstart covers the basics of training machine learning models, interpreting them, and deploying them to make predictions. \n\nWith Dataiku’s Visual Snowpark ML plugin - you won’t need to write a single line of code. That’s right!\n\n- Domain experts - you don’t need to fuss around with learning programming to inject ML into your team.\n- Data Scientists - we’re all trying to roll our own abstracted ML training and deployment platform - and we think you’ll like this one.\n\n\n\n### Use Case\nConsumer lending is difficult. What factors about an individual and their loan application could indicate whether they’re likely to pay back the loan? How can our bank optimize the loans approved and rejected based on our risk tolerance? \nWe’ll use machine learning to help with this decision making process. Our model will learn patterns between historical loan applications and default, then we can use it to make predictions for a fresh batch of applications. \n\n\n\n### What You’ll Learn\nThe exercises in this lab will walk you through the steps to: \n- Use Snowflake's \"Partner Connect\" to create a Dataiku cloud trial\n- Create a Snowpark-optimized warehouse (for ML workloads)\n- Upload a base project in Dataiku with our data sources in Snowflake\n- Look at our loan data, understand trends through correlation matrices\n- Train, interpret, and deploy Machine Learning models in Dataiku - powered by Snowpark ML\n- Use our trained model to make new predictions\n- `(Optional)` Set up an MLOps process to retrain the model, check for accuracy, and make new predictions on a weekly basis\n\n\n### What You’ll Need \n- Snowflake free 30-day trial environment\n- Dataiku free 14-day trial environment (via Snowflake Partner Connect)\n\n\n\u003C!-- ------------------------ --\u003E\n## Create Your Snowflake Lab Environment\n\nIf you haven't already, [register for a Snowflake free 30-day trial](https://trial.snowflake.com/) The rest of the sections in this lab assume you are using a new Snowflake account created by registering for a trial.\n\n\u003E \n\u003E \n\u003E  **Note**: Please ensure that you use the **same email address** for both your Snowflake and Dataiku sign up\n\n- **Region**  - Although not a requirement we'd suggest you select the region that is physically closest to you for this lab\n\n- **Cloud Provider**  -  Although not a requirement we'd suggest you select ```AWS``` for this lab\n\n- **Snowflake edition**  -  We suggest you select select the ```Enterprise edition``` so you can leverage some advanced capabilities that are not available in the Standard Edition.\n\n\n\nAfter activation, you will create a ```username```and ```password```. Write down these credentials. **Bookmark this URL for easy, future access**.\n\n\u003E \n\u003E \n\u003E  **About the screen captures, sample code, and environment:** \u003Cbr\u003E Screen captures in this lab depict examples and results that may slightly vary from what you may see when you complete the exercises.\n\n\n\u003C!-- ------------------------ --\u003E\n## Create Your Dataiku Lab Environment (Via Snowflake Partner Connect)\n\n\n### Log Into the Snowflake User Interface (UI)\n\nOpen a browser window and enter the URL of your Snowflake 30-day trial environment. You should see the login screen below. Enter your unique credentials to log in.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC1.png)\n\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\nYou may see \"welcome\" and \"helper\" boxes in the UI when you log in for the first time. Close them by clicking on Skip for now in the bottom right corner in the screenshot below.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC2.png)\n\n### Create Dataiku trial via Partner Connect\n\nAt the top right of the page, confirm that your current role is `ACCOUNTADMIN`, by clicking on your profile on the top right.\n\n1. Click on `Data Products` on the left-hand menu\n2. Click on `Partner Connect`\n3. Search for Dataiku\n4. Click on the `Dataiku` tile \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC3.png)\n\n\u003E \n\u003E Depending on which screen you are on you may not see the full menu as above but hovering over \n\u003E the Data Products (Cloud) icon will show the options\n\nThis will automatically create the connection parameters required for Dataiku to connect to Snowflake. Snowflake will create a dedicated database, warehouse, system user, system password and system role, with the intention of those being used by the Dataiku account.\n\nFor this lab we’d like to use the **PC_DATAIKU_USER** to connect from Dataiku to Snowflake, and use the **PC_DATAIKU_WH** when performing activities within Dataiku that are pushed down into Snowflake.\n\nThis is to show that a Data Science team working on Dataiku and by extension on Snowflake can work completely independently from the Data Engineering team that works on loading data into Snowflake using different roles and warehouses.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC4.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n1. Click `Connect`\n2. You will get a pop-ip which tells you your partner account has been created. Click on `Activate`\n\n\u003E \n\u003E \n\u003E  **Informational Note:** \u003Cbr\u003E If you are using a different Snowflake account than the one created \n\u003E at the start, you may get a screen asking for your email details. Click on ‘Go to Preferences’ and \n\u003E populate with your email details\n\nThis will launch a new page that will redirect you to a launch page from Dataiku.\n\nHere, you will have two options:\n\n1. Login with an existing Dataiku username\n2. Sign up for a new Dataiku account\n\n* We assume that you’re new to Dataiku, so ensure the `“Sign Up” box is selected`, and sign up with either GitHub, Google or your email address and your new password. \n\n**Make sure you use the same email address that you used for your Snowflake trial**.\n\n- `Click sign up`.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC5.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\nWhen using your email address, ensure your password fits the following criteria:\n\n1. At least 8 characters in length\n2. Should contain:\n   Lower case letters (a-z)\n   Upper case letters (A-Z)\n   Numbers (i.e. 0-9)\n\nYou should have received an email from Dataiku to the email you have signed up with. Activate your Dataiku account via the email sent.\n\n### Review Dataiku Setup\n\nUpon clicking on the activation link, please briefly review the Terms of Service of Dataiku Cloud. In order to do so, please scroll down to the bottom of the page. \n* Click on `I AGREE` and then click on `NEXT`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC6.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Complete your sign up some information about yourself and then click on `Start`.\n\n\n* You will be redirected to the Dataiku Cloud Launchpad site. Click `GOT IT!` to continue.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC7.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\nThis is the Cloud administration console where you can perform tasks such as inviting other users to collaborate, add plugin extensions, install industry solutions to accelerate projects as well as access community and academy resources to help your learning journey. \n\n\u003E \n\u003E**NOTE:** It may take several minutes for your instance to Dataiku to start up the first time,\n\u003E during this time you will not be able to add the extension as described below.\n\u003E You can always come back to this task later if time doesn't allow now\n\n### Add the Visual SnowparkML Plugin\n\nIt's beyond the scope of this course to cover plugins in depth but for this lab we would like to enable a few the Visual SnowparkML plugin so lets do that now.\n\n1. Click on `Plugins` on the left menu\n2. Select `+ ADD A PLUGIN` \n3. Find `Visual SnowparkML`\n4. Check `Install on my Dataiku instance`, and click `INSTALL`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC8.png)\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC9.png)\n\n1. Click on `Code Envs` on the left menu\n2. Select `ADD A CODE ENVIRONMENT` \n3. Select  `NEW PYTHON ENV`\n4. Name your code env `py_39_snowpark` **NOTE: The name must match exactly** \n5. Click `CREATE`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC10.png)\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC11.png)\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC12.png)\n\n6. Select `Pandas 1.3 (Python 3.7 and above) from Core Packages menu\n7. Add the following packages\n```sql\nscikit-learn==1.3.2\nmlflow==2.9.2\nstatsmodels==0.12.2\nprotobuf==3.16.0\nxgboost==1.7.3\nlightgbm==3.3.5\nmatplotlib==3.7.1\nscipy==1.10.1\nsnowflake-snowpark-python==1.14.0\nsnowflake-snowpark-python[pandas]==1.14.0\nsnowflake-connector-python[pandas]==3.7.0\nMarkupSafe==2.0.1\ncloudpickle==2.0.0\nflask==1.0.4\nJinja2==2.11.3\nsnowflake-ml-python==1.5.0\n```\n8. Select `rebuild env` from the menu on the left\n9. Click `Save and update`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/PC13.png)\n\nYou've now successfully set up your Dataiku trial account via Snowflake's Partner Connect. We are now ready to continue with the lab. For this, move back to your Snowflake browser.\n\n\u003C!-- ------------------------ --\u003E\n## Create a Snowpark Optimized Warehouse in Snowflake\n\n### Return to the Snowflake UI\n\nWe will now create an optimized warehouse\n\n1. Click `Admin` from the bottom of the left hand menu\n2. Then `Warehouses`\n3. Then click `+ Warehouse` in the top right corner\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH1.png)\n\nOnce in the `New Warehouse` creation screen perform the following steps:\n\n1. Create a new warehouse called `SNOWPARK_WAREHOUSE`\n2. For the type select `Snowpark-optimized`\n3. Select `Medium` as the size\n4. Lastly click `Create Warehouse`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH2.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Select your new Snowflake Warehouse by `clicking on it once`.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH3.png)\n\nWe need to permission the Dataiku Role that was created by Partner Connect in the earlier chapter for this new warehouse.\n- Scroll down to Privileges, and click `+ Privilege`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH4.png)\n\n1. For the Role select the role `PC_DATAIKU_ROLE`\n2. Under Pivileges grant the `USAGE` privilege\n3. Click on `Grant Privileges`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH5.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\nYou should now see your new privileges have been applied \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/OWH6.png)\n\n\n\n\u003C!-- ------------------------ --\u003E\n## Import a Baseline Dataiku Project\n\n\nReturn to the Dataiku trial launchpad in your browser\n\n1. Ensure you are on the `Overview` page\n2. Click on `OPEN INSTANCE` to get started.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/IMP1.png)\n\n\nCongratulations you are now using the Dataiku platform! For the remainder of this lab we will be working from this environment which is called the design node, its the pre-production environment where teams collaborate to build data products.\n\nNow lets import our first project.\n\n* Download the project zip file to your computer - **Don’t unzip it!** \n\n\u003Cbutton\u003E\n\n  [Starter Project](https://dataiku-partnerships.s3.eu-west-1.amazonaws.com/LOANDEFAULTSSNOWPARKML.zip)\n\u003C/button\u003E\n\nOnce you have download the starter project we can create our first project\n\n1. Click `+ NEW PROJECT` \n2. Then `Import project` \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/IMP2.png)\n\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Choose the .zip file you just downloaded, then click `IMPORT`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/IMP3.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\nYou should see a project with 4 dataset - two local CSVs which we’ve then imported into Snowflake\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/IMP4.png)\n\nNow that we have all our setup done, lets start working with our data.\n\u003C!-- ------------------------ --\u003E\n## Analyze trends in the data\n\n\nBefore we begin analyzing the data in our new project lets take a minute to understand some of the concepts and terminology of a project in Dataiku.\n\nHere is the project we are going to build along with some annotations to help you understand some key concepts in Dataiku. \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends0.png)\n\n* A **dataset** is represented by a blue square with a symbol that depicts the dataset type or connection. The initial datasets (also known as input datasets) are found on the left of the Flow. In this project, the input dataset will be the one we created in the first part of the lab.\n\n* A **recipe** in Dataiku DSS (represented by a circle icon with a symbol that depicts its function) can be either visual or code-based, and it contains the processing logic for transforming datasets. In addition to the core Visual and Code recipes Dataiku can be expanded with the use of plugins which are either from the freely available Dataiku library or developed by users. We will use the Visual SnowparkML plugin today\n\n* **Machine learning processes** are represented by green icons.\n\n* The **Actions Menu** is shown on the right pane and is context sensitive.\n\n* Whatever screen you are currently in you can always return to the main **Flow** by clicking the **Flow** symbol from the top menu (also clicking the project name will take you back to the main Project page).\n\n\u003E \n\u003E You can refer back to this completed project screenshot if you want to check your progress through the lab.\n\n* `Double click` into the `LOAN_REQUESTS_KNOWN_SF` dataset. This is our dataset of historical loan applications, a number of attributes about them, and whether the loan was paid back or defaulted (the DEFAULTED column - 1.0 = default, 0.0 = paid back).\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends1.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n1. Click the `Statistics` tab on the top\n2. Next click `+ Create first worksheet`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends2.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Then select `Automatically suggest analyses`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends3.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Choose a few of the suggestions, be sure to include the Correlation Matrix in your selections then click `CREATE SELECTED CARDS`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends4.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n**Question:** What trends do you notice in the data? \n\nLook at the correlation matrix, and the DEFAULTED row. Notice that INTEREST_RATE has the highest correlation with DEFAULTED. We should definitely include this feature in our models!\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Trends5.png)\n\n**Positive** correlation means as INTEREST_RATE rises, so does DEFAULTED (higher interest rate -\u003E higher probability of default).\nNotice MONTHLY_INCOME has a **negative** correlation to DEBT_TO_INCOME_RATIO. This means that as monthly income goes up, applicants’ debt to income ratio generally goes down.\n\nSee if you can identify a few other features we should include in our models.\n\n\n\u003C!-- ------------------------ --\u003E\n## Train Machine Learning Models\n\n\n### Create a new Visual SnowparkML recipe\n\nNow we will tran an ML model using our plugin. Return to the flow either by clicking on the flow icon or by using the keyboard shortcut `(g+f)`\n\n1. From the Flow click once on the `LOAN_REQUESTS_KNOWN_SF` dataset.\n2. From the `Actions menu` on the right scroll down and select the `Visual Snowpark ML plugin`\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML2.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Click Train ML Models on Snowpark\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML3.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\nWe now need to set our three `Outputs` \n\n-  Click on `Set` under the  `Generated Train Dataset`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML4.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n1. Set the name to `train`\n2. Select `PC_DATAIKU_DB` to store into\n3. Click `CREATE DATASET`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML5.png)\n\nWe will now repeat this process for the other two outputs\n\n-  Click on `Set` under the  `Generated Test Dataset`\n1. Set the name to `test`\n2. Select `PC_DATAIKU_DB` to store into\n3. Click `CREATE DATASET`\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML6.png)\n\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n*  Click on `Set` under the  `Models Folder`\n1. Set the name to `models`\n2. Select `dataiku-managed-storage` to store into\n3. Click `CREATE FOLDER`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML7.png)\n\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Your three outputs should now look like the image below. Finally click on `CREATE`\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML8.png)\n\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n### Define model training settings\n\nLets fill out the parameters for our training session.\n\n1. Give your model a name\n2. Choose `DEFAULTED` as the target column\n3. Select `Two-class classification` as the prediction type\n4. Choose `ROC AUC` as our model metric. This is a common machine learning metric for classification problems.\n\nLeave the Train ratio and random seed as is. This will split our input dataset into 80% of records for training, leaving 20% for an unbiased evaluation of the model\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML9.png)\n\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Choose the following features to include in our model. Make sure to make a selection for Encoding / Rescaling and Impute Missing Values With - **don’t leave them empty**.\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML13.png)\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML14.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Choose the following algorithms to start. We’ll go through the basics of these algorithms after we kick off our model training.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML15.png)\n\n1. Leave the Search space limit as 4\n2. Write SNOWPARK_WAREHOUSE to use the Snowpark-optimized warehouse we created earlier.\n3. Check the “Deploy to Snowflake ML Model Registry” box. This will deploy our best trained model to Snowflake’s Model Registry - where we can use it to make predictions later on.\n\n\n\n* Finally Click the `RUN` button in the bottom left hand corner to start training our models.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/ML16.png)\n\n\n\u003C!-- ------------------------ --\u003E\n## Machine Learning - Basic Theory (Optional)\n\n\nWhile we’re waiting for our models to train, let’s learn a bit about machine learning. This is an oversimplification of some complicated topics. If you’re interested there are links at the end of the course for the Dataiku Academy and many other free resources online.\n\n### Machine Learning, Classification, and Regression\n\n**Machine learning** - the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.\n\n**Oversimplified definition of machine learning** - Fancy pattern matching based on the data you feed into it\n\nThe two most common types of machine learning solutions are supervised and unsupervised learning.\n\u003Cbr\u003E\n**Supervised learning**\nGoal: predict a target variable\n- category = classification\n- numerical value = regression\n\nExamples:\n- Predict the sales price of an apartment (regression)\n- Forecast the winner of an election (classification)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n**Unsupervised learning**\nGoal: identify patterns\n- Group similar individuals  = **clustering**\n- Find anomalies = **anomaly detection**\n\nExamples:\n- Segment consumers according to their behavior (clustering)\n- Find anomalous opioid shipments from a DEA database (anomaly detection)\n\u003Cbr\u003E\n\u003Cbr\u003E\nOur problem - predicting loan defaults, is a **supervised, classification problem**.\n\nWe need a structured dataset to train a model, in particular: \n- Rows measuring **individual observations** (one transaction per row)\n- **Target column** with real labels of what we want to predict\n- **Other columns** (features) that the model can use to predict the target (through fancy pattern matching)\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB1.png)\n\u003Cbr\u003E\n### Train / Test split\n\nOnce we have a structured dataset with observations, a target, and features, we split it into train and test sets\n\nWe could split it:\n- Randomly\n- Based on time\n- Other criteria\n\nA random split of 80% train / 20% test is common\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB2.png)\n\u003Cbr\u003E\n\nWe train models on the train set, then evaluate them on the test set. This way, we can simulate how the model will perform on data that it hasn’t seen before.\n\n### Feature Handling\n\nTwo keys to choosing features to include in our model:\n- Only include variables that you know you’ll have at the time you need to make predictions (e.g. at time of sale for future credit card transactions)\n- Use your domain knowledge - if you think a variable could be a driver of the target, include it in your model!\n\nMost machine learning models require variables to be a specific format to be able to find patterns in the data.\n\nWe can generally break up our variables into two categories:\n- **Numeric** - e.g. AMOUNT_REQUESTED, DEBT_TO_INCOME_RATIO\n- **Categorical** - e.g. LOAN_PURPOSE, STATE\n\nHere are some ways to transform these types of features:\n\n**Numeric** - e.g. AMOUNT_REQUESTED, DEBT_TO_INCOME_RATIO\n\u003Cbr\u003E\n\nThings you typically want to consider:\n- **Impute** a number for rows missing values. Average, median are common\n- **Rescale** the variable. Standard rescaling is common (this transforms a value to its Z-score)\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB3.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n**Categorical** - e.g. LOAN_PURPOSE, STATE\n\u003Cbr\u003E\n\nThings you typically want to consider:\n- **Encode** values with a number. Dummy encoding, ordinal encoding are common\n- **Impute** a value for rows missing values. You can treat a missing value as its own category, or impute with the most common value.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB4.png)\n\n\u003Cbr\u003E\n\n### Machine Learning Algorithms \n\u003Cbr\u003E\n\nLet’s go through a few common machine learning algorithms.\n\n**Linear Regression**\n\nFor linear regression (predicting a number), we find the line of best fit, plotting our feature variables and our target\n\n`y = b0 + b1 * x`\n\nIf we were training a model to predict exam scores based on # hours of study, we would solve for this equation\n\n`exam_score = b0 + b1 * (num_hours_study)`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB5.png)\n\u003Cbr\u003E\n\nWe use math (specifically a technique called Ordinary Least Squares[1]) to find the b0 and b1 of our best fit line\n\n`exam_score = b0 + b1 * (num_hours_study)`\n\n`exam_score = 32 + 8 * (num_hours_study)`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB6.png)\n\u003Cbr\u003E\n\n\n**Logistic Regression**\n\nLogistic regression is similar to linear regression - except built for a classification problem (e.g. loan default prediction).\n\n`log(p/1-p) = b0 + b1 * (num_hours_study)`\n\n`log(p/1-p) = 32 + 8 * (num_hours_study)`\n\n`p = probability of exam success`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB7.png)\n\n\u003Cbr\u003E\n\n**Decision Trees**\n\nImagine our exam pass/fail model with more variables.\n\nDecision trees will smartly create if / then statements, sending each row along a branch until it makes a prediction of your target variable\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB8.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n**Random Forest**\n\nA Random Forest model trains many decision trees, introduces randomness into each one, so they behave differently, then averages their predictions for a final prediction\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB9.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n**Overfitting**\n\nWe want our ML model to be able to understand true patterns in the data - uncover the signal, and ignore the noise (random, unexplained variation in the data)\n\n Overfitting is an undesirable machine learning behavior that occurs when the machine learning model gives accurate predictions for training data but not for new data\n\n ![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB10.png)\n \u003Cbr\u003E\n ![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/MLB11.png)\n\u003Cbr\u003E\n\n**How to control for overfitting**\n\nLogistic Regression\n- Increasing C shrinks your equation coefficients\n- Increase C to control more for overfitting\n\nExample\n\n`C = 0.01:  log(p/1-p) = 32 + 8 * (num_hours_study) + 6 * (num_hours_sleep)`\n \n`C = 0.1:  log(p/1-p) = 32 + 5 * (num_hours_study) + 4 * (num_hours_sleep) `\n\n`C = 1:  log(p/1-p) = 32 + 3 * (num_hours_study) + 2 * (num_hours_sleep) `\n\n`C = 10:  log(p/1-p) = 32 + 2 * (num_hours_study) + 0 * (num_hours_sleep) `\n\n`C = 100:  log(p/1-p) = 32 + 1 * (num_hours_study) + 0 * (num_hours_sleep)` \n\u003Cbr\u003E\n\u003Cbr\u003E\n\nRandom Forest\n\u003Cbr\u003E\n\n- **Maximum depth of tree**  how far down can each decision tree go?\n- Decrease this to control more for overfitting\n\nFor more in-depth tutorials and self-paced machine learning courses see the links to Dataiku's freely available Academy in the last chapter of this course\n\n\u003C!-- ------------------------ --\u003E\n## Model Evaluation\n\n\nOnce we’ve trained our models, we’ll want to take a deeper dive deep into how they’re performing, what features they’re considering, and whether they may be biased. Dataiku has a number of tools for evaluating models.\n\n- `Double click` on your model (Green diamond) from the flow\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval1.png)\n\n* You’ll see your best trained model here. `Click` into it.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval2.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n1. Select `Feature Importance` from the menu on the left side\n2. Then click `COMPUTE NOW`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval3.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\nHere we can see that the top 3 features impacting the model are applicants’ FICO scores, the interest rate of the loan, and the amount requested. This makes sense!\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval4.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\nScroll down on this page - you’ll see the directional effect of each feature on default predictions. You can see that the higher FICO scores generally mean lower probability of default.\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval5.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Click on the `Confusion matrix` tab from the menu on the left. \n\nHere we can see how the model would have performed on the hold-out test set of loan applicants. Notice that my model is very good at catching defaulters (83 Predicted 1.0 out of 84 Actually 1.0), at the expense of mistakenly rejecting 124 applicants that would have paid back their loan. \n\nTry moving the threshold bar back and forth. It will cause the model to be more or less sensitive. Based on your business problem, you may want a higher or lower threshold.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Eval6.png)\n\n\n\n\u003C!-- ------------------------ --\u003E\n\n## Make Predictions (Scoring)\n\n\nUsing a machine learning model to make predictions is called `scoring` or `inference`\n\n### Score the unknown loan applications using the trained model\n\n1. Go to the project Flow, click once on the ``LOAN_REQUESTS_UNKNOWN_SF`` dataset\n2. Then click on the ``Visual Snowpark ML`` plugin from the right hand Actions menu.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score1.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Click `Score New Records using Snowpark`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score2.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\nWe need to add our model as an input and set an output dataset for the results of the scoring.\n\n1. In the `Inputs` under the `Saved Model` option click on `SET` to add your saved model\n2. In the `Outputs` section under `Scored Dataset Option` click on `SET` and give your output dataset a name\n3. For `Store into` use the `PC_DATAIKU_DB** connection\n4. Click on `CREATE DATASET`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score5.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Your screen should now look like this. Go ahead and `click on CREATE`\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score6.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n* Make sure the warehouse you created earlier `SNOWPARK_WAREHOUSE` is selected then click on `RUN`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score7.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\nWhen it finishes, your flow should look like this\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score8.png)\n\n* `Double click` into the output scored dataset - scroll to the right, and you should see predictions of whether someone is likely to pay back their loan or not!\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/Score9.png)\n\n\n\u003C!-- ------------------------ --\u003E\n\n## MLOps (optional)\n\n\nLet’s say we want to automatically run new loan applications through our model every week on Sunday night.\n\nAssume that `LOAN_REQUESTS_UNKNOWN` is a live dataset of new loan applications that is updated throughout the week.\n\nWe want to rerun all the recipes leading up to unknown_loans_scored, where our model makes predictions.\n\n### Build a weekly scoring scenario\n\n- Click into the Scenarios tab\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops1.png)\n\n- Click `+ CREATE YOUR FIRST SCENARIO`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops2.png)\n\n- Name your scenario something like `“Weekly Loan Application Scoring”`\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops3.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n- Add a time-based trigger\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops4.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n- Set the trigger to run `every week on Sunday at 9pm`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops5.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n- In the `Steps` tab, click `Add Step`, then `Build / Train`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops6.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n- Add a dataset to build\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops7.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n- Then choose the `unknown_loans_scored` dataset\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops8.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\n- Check the `Force-build` button to recursively build all datasets leading up to `unknown_loans_scored`, then click the `run` button to test it out.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops9.png)\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\u003Cbr\u003E\n\nYou’ll be able to see scenario run details in the “Last runs” tab\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/mlops10.png)\n\n### Build a monthly model retraining scenario (optional)\n\nIt’s good practice to retrain machine learning models on a regular basis with more up-to-date data. The world changes around us; the patterns of loan applicant attributes affecting default probability are likely to change too.\n\nIf you have time you can assume that LOAN_REQUESTS_KNOWN is a live dataset of historical loan applications that is updated with new loan payback and default data on an ongoing basis.\n\nYou can automatically retrain your model every month with scenarios, and put in a AUC check to make sure that the model is performing and build the scored dataset\n\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion & Resources\n\nCongratulations on completing this introductory lab exercise! Congratulations! You've mastered the Snowflake basics and you’ve taken your first steps toward a no-code approach to training machine learning models with Dataiku.\n\nYou have seen how Dataiku's deep integrations with Snowflake can allow teams with different skill sets get the most out of their data at every stage of the machine learning lifecycle.\n\nWe encourage you to continue with your free trial and continue to refine your models and by using some of the more advanced capabilities not covered in this lab.\n\n### What You Learned:\n\n- Use Snowflake's \"Partner Connect\" to create a Dataiku cloud trial\n- Create a Snowpark-optimized warehouse (for ML workloads)\n- Upload a base project in Dataiku with our data sources in Snowflake\n- Look at our loan data, understand trends through correlation matrices\n- Train, interpret, and deploy Machine Learning models in Dataiku - powered by Snowpark ML\n- Use our trained model to make new predictions\n- `(Optional)` Set up an MLOps process to retrain the model, check for accuracy, and make new predictions on a weekly basis\n\n### Related Resources\n\n- Join the [Snowflake Community](https://community.snowflake.com/s/)\n- Join the [Dataiku Community](https://community.dataiku.com/)\n- Sign up for [Snowflake University](http://https://community.snowflake.com/s/snowflake-university)\n- Join the [Dataiku Academy](https://academy.dataiku.com/)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-b4ca659537","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-774e0b1dc8",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-ab1aea41b6","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-8f93dba295","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-f9a8f54528",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-110cf0bada",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-5ac6f9ad0c","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-bf8361167e","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ECreate Your Snowflake Lab Environment\u003C/h2\u003E","\u003Ch2\u003ECreate Your Dataiku Lab Environment (Via Snowflake Partner Connect)\u003C/h2\u003E","\u003Ch2\u003ECreate a Snowpark Optimized Warehouse in Snowflake\u003C/h2\u003E","\u003Ch2\u003EImport a Baseline Dataiku Project\u003C/h2\u003E","\u003Ch2\u003EAnalyze trends in the data\u003C/h2\u003E","\u003Ch2\u003ETrain Machine Learning Models\u003C/h2\u003E","\u003Ch2\u003EMachine Learning - Basic Theory (Optional)\u003C/h2\u003E","\u003Ch2\u003EModel Evaluation\u003C/h2\u003E","\u003Ch2\u003EMake Predictions (Scoring)\u003C/h2\u003E","\u003Ch2\u003EMLOps (optional)\u003C/h2\u003E","\u003Ch2\u003EConclusion & 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class=\"sf-footer__column-title\"\u003EProduct\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/platform/\"\u003EPlatform\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/data-engineering/\"\u003EData Engineering\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/analytics/\"\u003EAnalytics\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/ai/\"\u003EAI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/applications-and-collaboration/\"\u003EApplications & Collaboration\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca 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