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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-1aead89566","isDeveloperGuidesPage":false,"quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroForkRepoLink":{"id":"button-108cc59fdd","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/hex-churn-model"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"},"quickstartHeroTitle":{"lines":["Churn modeling using Snowflake and Hex"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"gflomo@hex.tech","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"External 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Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}],"fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/hex-churn-model",":type":"snowflake-site/components/quickstart/quickstart-hero"},"flexible_column_cont":{"id":"flexible-column-container-87b83a7d7e","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-72784c624c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-ea9adfdc98","description":"This lab will walk you through how to use Snowflake and Hex.","paragraphs":["\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EIn this Quickstart guide, we will play the role of a data scientist at a telecom company that wants to identify users who are at high risk of churning. To accomplish this, we need to build a model that can learn how to identify such users. We will demonstrate how to use Hex in conjunction with Snowflake/Snowpark to build a Random Forest Classifier to help us with this task.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with basic Python and SQL\u003C/li\u003E\u003Cli\u003EFamiliarity with training ML models\u003C/li\u003E\u003Cli\u003EFamiliarity with data science notebooks\u003C/li\u003E\u003Cli\u003EGo to the \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake\u003C/a\u003E sign-up page and register for a free account. After registration, you will receive an email containing a link that will take you to Snowflake, where you can sign in.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to import/export data between Hex and Snowflake\u003C/li\u003E\u003Cli\u003EHow to train a Random Forest with Snowpark ML model\u003C/li\u003E\u003Cli\u003EHow to visualize the predicted results from the forecasting model\u003C/li\u003E\u003Cli\u003EHow to convert a Hex project into an interactive web app and make predictions on new users\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetting up partner connect\u003C/h2\u003E\n","\u003Cp\u003EAfter logging into your Snowflake account, you will land on the \u003Ccode\u003ELearn\u003C/code\u003E page. To connect with Hex, navigate to the \u003Ccode\u003EAdmin\u003C/code\u003E tab on the left and click on \u003Ccode\u003EPartner connect\u003C/code\u003E. In the search bar at the top, type \u003Ccode\u003EHex\u003C/code\u003E and the Hex partner connect tile will appear. Clicking on the tile will bring up a new screen, and click the \u003Ccode\u003Econnect button\u003C/code\u003E in the lower right corner. A new screen will confirm that your account has been created, from which you can click \u003Ccode\u003EActivate\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/pc.gif\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreating a workspace\u003C/h3\u003E\n","\u003Cp\u003EAfter activating your account, you'll be directed to Hex and prompted to create a new workspace and give it a name. Once you've named your workspace, you'll be taken to the projects page where you can create new projects, import existing projects (Hex or Jupyter), and navigate to other sections of your workspace.\u003C/p\u003E\n","\u003Ch2\u003EGetting Started with Hex\u003C/h2\u003E\n","\u003Cp\u003ENow we can move back over to Hex and get started on our project. The first thing you'll need to do is get the Hex project that contains all of the code we'll work through to train our model.\u003C/p\u003E\n","\u003Cp\u003EClicking this button will copy the template project into your new workspace.\u003C/p\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://app.hex.tech/new-project?signup=true&baseHexId=057d76c5-b7c3-465e-9e61-f939106e7c5d&baseOrgId=hex-public\"\u003EDuplicate Hex project\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E</button>\u003C/p\u003E\n","\u003Cp\u003ENow that you've got your project imported, you will find yourself in the \u003Ca href=\"https://learn.hex.tech/docs/explore-data/projects/projects-introduction#the-notebook-view\"\u003ELogic view\u003C/a\u003E of a Hex project. The Logic view is a notebook-like interface made up of cells such as code cells, markdown cells, input parameters and more! On the far left side, you'll see a control panel that will allow you to do things like upload files, import data connections, or search your project.\u003C/p\u003E\n","\u003Cp\u003EBefore we dive into the code, we'll need to import our Snowflake data connection, which has been automatically created by the partner connect process.\u003C/p\u003E\n","\u003Cp\u003EHead over to the Data sources tab represented by a database icon with a lightning bolt. You should see two data connections - [Demo] Hex public data and Snowflake. Import both connections.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/DC.gif\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOne nice feature of Hex is the \u003Ca href=\"https://learn.hex.tech/docs/explore-data/projects/project-execution/execution-model#reactive-graph-based-execution-model\"\u003Ereactive execution model\u003C/a\u003E. This means that when you run a cell, all related cells are also executed so that your projects are always in a clean state. However, if you want to ensure you don’t get ahead of yourself as we run through the tutorial, you can opt to turn this feature off. In the top right corner of your screen, you’ll see a Run mode dropdown. If this is set to Auto, select the dropdown and change it to cell only.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/mode.gif\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the walkthrough video of the lab, we opt to leave this on and instead un-comment out the template code as we work through the project.\u003C/p\u003E\n","\u003Ch2\u003EReading and writing data\u003C/h2\u003E\n","\u003Cp\u003ETo predict customer churn, we first need data to train our model. In the SQL cell labeled \u003Cstrong\u003EPull churn results\u003C/strong\u003E assign \u003Ccode\u003E[Demo] Hex public data\u003C/code\u003E as the data connection source and run the cell.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/pull_data.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAt the bottom of this cell, you will see a green output variable labeled \u003Ccode\u003Edata\u003C/code\u003E. This is a Pandas dataframe, and we are going to write it back into our \u003Ccode\u003ESnowflake\u003C/code\u003E data connection. To do so, input the following configurations to the writeback cell (labeled: \u003Cstrong\u003EWriteback to snowflake)\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/writeback.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESource\u003C/strong\u003E: \u003Ccode\u003Edata\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConnection\u003C/strong\u003E: \u003Ccode\u003ESnowflake\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E: \u003Ccode\u003EPC_HEX_DB\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema\u003C/strong\u003E: \u003Ccode\u003EPublic\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETable\u003C/strong\u003E: \u003Cem\u003EStatic\u003C/em\u003E and name it \u003Ccode\u003ECHURN_DATA\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOnce the config is set, enable \u003Ccode\u003ELogic session\u003C/code\u003E as the writeback mode (in the upper right of the cell) and run the cell.\u003C/p\u003E\n","\u003Cp\u003EIn the SQL cell labeled \u003Cstrong\u003EChurn data\u003C/strong\u003E, change the data source to \u003Ccode\u003ESnowflake\u003C/code\u003E and execute the cell. You will see a green output variable named data at the bottom.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/sf_churn.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EData preparation\u003C/h2\u003E\n","\u003Cp\u003ENow that we have our data in Hex, we want to make sure the it’s clean enough for our machine learning algorithm. To ensure this, we’ll first check for any null values.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edata.isnull().sum()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow that we have checked for null values, let's look at the distribution of each variable.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# create a 15x5 figure\nplt.figure(figsize=(15, 5))\n\n# create a new plot for each variable in our dataframe\nfor i, (k, v) in enumerate(data.items(), 1):\n    plt.subplot(3, 5, i)  # create subplots\n    plt.hist(v, bins = 50)\n    plt.title(f'{k}')\n\nplt.tight_layout()\nplt.show();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/chart.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe charts' results allow us to visualize each variables distribution, which can help us identify early signs of skewness, among other things. We can see that all of our continuous variables follow a fairly normal distribution, and further transformations won't be necessary. The \u003Ccode\u003EDataUsage\u003C/code\u003E column appears a little off because many customers use 0GB of data, but there are also a lot of users who didn't have data plans in the first place, so this isn't considered an anomaly.\u003C/p\u003E\n","\u003Ch2\u003EUnderstanding churn rate\u003C/h2\u003E\n","\u003Cp\u003EIf you take a look at our visuals, you may notice that the churn chart looks a little odd. Specifically, it looks like there are a lot more users who haven’t churned than who have.\u003C/p\u003E\n","\u003Cp\u003EWe take a closer look at this by visualizing in a chart cell.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/imbal.png\" alt=\"\"\u003E\nAs you can see, the majority of observations are in support of user who haven’t yet churned. Specifically, 85% of user haven’t churned while the other 15% has. In machine learning, a class imbalance such as this can cause issues when evaluating the model since it’s not getting equal attention from each class. In the next section we will go over a method to combat the imbalance problem.\u003C/p\u003E\n","\u003Ch2\u003EEstablishing a Snowpark connection\u003C/h2\u003E\n","\u003Cp\u003ENow, we can connect to our Snowflake connection that we imported earlier. To do this head over to the data sources tab on the left control panel to find your Snowflake connection. If you hover your mouse over the connection and click on the dropdown next to the \u003Ccode\u003Equery\u003C/code\u003E button and select \u003Ccode\u003Eget Snowpark session\u003C/code\u003E. This will create a new cell for us with all the code needed to spin up a Snowpark session.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/snowpark.gif\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo ensure we don't run into any problems down the line, paste in the following line at the bottom of the cell.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esession.use_schema("PC_HEX_DB.PUBLIC")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EFeature engineering\u003C/h2\u003E\n","\u003Cp\u003EIn order to predict the churn outcomes for customers not in our dataset, we’ll need to train a model that can identify users who are at risk of churning from the history of users who have. However, it was mentioned in the last section that there is an imbalance in the class distribution that will cause problems for our model if not handled properly. One way to handle this is to create new data points such that the classes balance out. This is also known as upsampling.\u003C/p\u003E\n","\u003Cp\u003EFor this, we’ll be using the \u003Ccode\u003ESMOTE\u003C/code\u003E algorithm from the \u003Ccode\u003Eimblearn\u003C/code\u003E package.\u003C/p\u003E\n","\u003Cp\u003EFirst, we'll get our features— aka everything except the target column, Churn. We do this in a SQL cell, making use of the SELECT / EXCLUDE syntax in Snowflake! Uncomment + run the SQL cell called "Features".\u003C/p\u003E\n","\u003Cp\u003ENow, we'll upsample. We could do this locally using SMOTE, but we want this entire workflow to run in Snowpark end-to-end, so we're going to create a stored procedure to do our upsampling.\u003C/p\u003E\n","\u003Cp\u003ERun the code cell labeled \u003Cstrong\u003EUpsampling the data\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef upsample(\n    session: Session,\n    features_table_name: str,\n) -> str:\n\n    import pandas as pd\n    import sklearn\n    from imblearn.over_sampling import SMOTE\n\n    features_table = session.table(features_table_name).to_pandas()\n    X = features_table.drop(columns=["Churn"])\n    y = features_table["Churn"]\n    upsampler = SMOTE(random_state=111)\n    r_X, r_y = upsampler.fit_resample(X, y)\n    upsampled_data = pd.concat([r_X, r_y], axis=1)\n    upsampled_data.reset_index(inplace=True)\n    upsampled_data.rename(columns={'index': 'INDEX'}, inplace=True)\n\n\n\n    upsampled_data_spdf = session.write_pandas(\n        upsampled_data,\n        table_name=f'{features_table_name}_SAMPLED',\n        auto_create_table=True,\n        table_type='', # creates a permanent table\n        overwrite=True,\n    )\n\n    return "Success"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we have created a function that will be our stored procedure. It will perform upsampling, and write the upsampled data back to a new Snowflake table called '{features_table_name}_SAMPLED'.\u003C/p\u003E\n","\u003Cp\u003ENow we create + call the stored procedure, in 2 more python cells:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esession.sql('CREATE OR REPLACE STAGE SNOWPARK_STAGE').collect()\n\nsession.sproc.register(\n    upsample,\n    name="upsample_data_with_SMOTE",\n    stage_location='@SNOWPARK_STAGE',\n    is_permanent=True,\n    execute_as='caller',\n    packages=['imbalanced-learn==0.10.1', 'pandas', 'snowflake-snowpark-python==1.6.1', 'scikit-learn==1.2.2'],\n    replace=True,\n)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# # here we call the sproc to perform the upsampling\nsession.call('upsample_data_with_SMOTE', 'TELECOM_CHURN')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow all that's left is to query the upsampled data from the table using another SQL cell, in this case the Features upsampled cell. If you prefer, you could change your stored procedure to return a Snowpark DataFrame directly rather than writing back a permanent table, but if you are going to be doing continued work on this dataset, writing a permanent table may make more sense.\u003C/p\u003E\n","\u003Cp\u003ENow that we have balanced our dataset, we can prepare our model for training. The model we have chosen for this project is a Random Forest classifier. A random forest creates an ensemble of smaller models that all make predictions on the same data. The prediction with the most votes is the prediction the model chooses.\u003C/p\u003E\n","\u003Cp\u003ERather than use a typical random forest object, we'll make use of Snowflake ML. Snowflake ML offers capabilities for data science and machine learning tasks within Snowflake. It provides estimators and transformers compatible with scikit-learn and xgboost, allowing users to build and train ML models directly on Snowflake data. This eliminates the need to move data or use stored procedures. It uses wrappers around scikit-learn and xgboost classes for training and inference, ensuring optimized performance and security within Snowflake.\u003C/p\u003E\n","\u003Cp\u003EIn the cell labeled \u003Ccode\u003ESnowflake ML model preprocessing\u003C/code\u003E we'll import Snowpark ML to further process our dataset to prepare it for our model.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport snowflake.ml.modeling.preprocessing as pp\nfrom snowflake.ml.modeling.ensemble import RandomForestClassifier\ndataset = features_upsampled\n\n# Get the list of column names from the dataset\nfeature_names_input = [c for c in dataset.columns if c != '"Churn"' and c != "INDEX"]\n\n\n# Initialize a StandardScaler object with input and output column names\nscaler = pp.StandardScaler(\n    input_cols=feature_names_input,\n    output_cols=feature_names_input\n)\n\n# Fit the scaler to the dataset\nscaler.fit(dataset)\n\n# Transform the dataset using the fitted scaler\nscaled_features = scaler.transform(dataset)\n\n# Define the target variable (label) column name\nlabel = ['"Churn"']\n\n# Define the output column name for the predicted label\noutput_label = ["predicted_churn"]\n\n# Split the scaled_features dataset into training and testing sets with an 80/20 ratio\ntraining, testing = scaled_features.random_split(weights=[0.8, 0.2], seed=111)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAccepting Anaconda terms\u003C/h3\u003E\n","\u003Cp\u003EBefore we can train the model, we'll need to accept the Anaconda terms and conditions.\nTo do this, navigate back to Snowflake and click on your username in the top left corner. You'll see a section that will allow you to switch to the \u003Ccode\u003EORGADMIN\u003C/code\u003E role. Once switched over, navigate to the \u003Ccode\u003EAdmin\u003C/code\u003E tab and select \u003Ccode\u003EBilling & Terms\u003C/code\u003E. From here, you will see a section that will allow you to accept the anaconda terms and conditions. Once this is done, you can head back over to Hex and run the cell that trains our model.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/vhol-accept-terms.gif\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EModel training\u003C/h2\u003E\n","\u003Cp\u003ENow we can train our model. Run the cell labeled \u003Ccode\u003EModel training\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Initialize a RandomForestClassifier object with input, label, and output column names\nmodel = RandomForestClassifier(\n    input_cols=feature_names_input,\n    label_cols=label,\n    output_cols=output_label,\n)\n\n# Train the RandomForestClassifier model using the training set\nmodel.fit(training)\n\n# Predict the target variable (churn) for the testing set using the trained model\nresults = model.predict(testing)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/train.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the next section, we will look at how well our model performed as well as which features played the most important role when predicting the churn outcome.\u003C/p\u003E\n","\u003Ch2\u003EModel evaluation and feature importance\u003C/h2\u003E\n","\u003Cp\u003EIn order to understand how well our model performs at identifying users at risk of churning, we’ll need to evaluate how well it does predicting churn outcomes. Specifically, we’ll be looking at the recall score, which tells us \u003Cem\u003Eof all the customers that will churn, how many can it identify.\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003ERun the code cell labeled \u003Cstrong\u003EEvaluate model\u003C/strong\u003E on \u003Cem\u003Eaccuracy and recall.\u003C/em\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epredictions = results.to_pandas().sort_values("INDEX")[['predicted_churn'.upper()]].astype(int).to_numpy().flatten()\nactual = testing.to_pandas().sort_values("INDEX")[['Churn']].to_numpy().flatten()\n\naccuracy = round(accuracy_score(actual, predictions), 3)\nrecall = round(recall_score(actual, predictions), 3)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis will calculate an accuracy and recall score for us which we'll display in a \u003Ca href=\"https://learn.hex.tech/docs/explore-data/cells/visualization-cells/single-value-cells#single-value-cell-configuration\"\u003Esingle value cell\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/scores.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EFeature importance\u003C/h3\u003E\n","\u003Cp\u003ENext, we want to understand which features were deemed most important by the model when making predictions. Lucky for us, our model keeps track of the most important features, and we can access them using the \u003Ccode\u003Efeature_importances_\u003C/code\u003E attribute. Since we're using Snowflake-ml, we'll need to extract the original \u003Ccode\u003Esklearn\u003C/code\u003E object from our model. Then we can perform feature importance as usual.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Erf = model.to_sklearn()\nimportances = pd.DataFrame(\n    list(zip(features.columns, rf.feature_importances_)),\n    columns=["feature", "importance"],\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet’s visualize the most important features.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/important.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EPredicting churn for a new user\u003C/h2\u003E\n","\u003Cp\u003ENow is the moment we've all been waiting for: predicting the churn outcome for a new user. In this section, you should see an array of input parameters already in the project. Each of these inputs allow you to adjust a different feature that goes into predicting customer churn, which will simulate a new user. But we’ll still need to pass this data to our model, so how can we do that?\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/input.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EEach input parameter has its own variable as its output, and these variables can be referenced in a Python cell. The model expects the inputs it receives to be in a specific order otherwise it will get confused about what the features mean. Keeping this in mind, execute the Python cell labeled \u003Cstrong\u003E\u003Cem\u003ECreate the user vector\u003C/em\u003E\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Einputs = [\n\t    account_weeks,\n\t    1 if renewed_contract else 0, # This value is a bool and we need to convert to numbers\n\t    1 if has_data_plan else 0, # This value is a bool and we need to convert to numbers\n\t    data_usage,\n\t    customer_service_calls,\n\t    mins_per_month,\n\t    daytime_calls,\n\t    monthly_charge,\n\t    overage_fee,\n\t    roam_mins,\n]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis creates a list where each element represents a feature that our model can understand. However, before our model can accept these features, we need to transform our array. To do this, we will convert our list into a numpy array and reshape it so that there is only one row and one column for all features.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Euser_vector = np.array(inputs).reshape(1, -1)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs a last step, we’ll need to scale our features within the original range that was used during the training phase. We already have a scaler fit on our original data and we can use the same one to scale these features.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Euser_vector_scaled = scaler.transform(user_vector)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe final cell should look like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# get model inputs\nuser_vector = np.array([\n    account_weeks,\n    1 if renewed_contract else 0,\n    1 if has_data_plan else 0,\n    data_usage,\n    customer_service_calls,\n    mins_per_month,\n    daytime_calls,\n    monthly_charge,\n    overage_fee,\n    roam_mins,\n]).reshape(1,-1)\n\nuser_dataframe = pd.DataFrame(user_vector, columns=scaler.input_cols)\nuser_vector = scaler.transform(user_dataframe)\nuser_vector.columns = [column_name.replace('"', "") for column_name in user_vector.columns]\nuser_vector = session.create_dataframe(user_vector)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe are now ready to make predictions. In the last code cell labeled \u003Cstrong\u003E\u003Cem\u003EMake predictions and get results\u003C/em\u003E\u003C/strong\u003E, we will pass our user vector to the model's predict function, which will output its prediction. We will also obtain the probability for that prediction, allowing us to say: \u003Cstrong\u003E\u003Cem\u003E"The model is 65% confident that this user will churn."\u003C/em\u003E\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epredicted_value = model.predict(user_vector).toPandas()[['predicted_churn'.upper()]].values.astype(int).flatten()\nuser_probability = model.predict_proba(user_vector).toPandas()\nprobability_of_prediction = max(user_probability[user_probability.columns[-2:]].values[0]) * 100\nprediction = 'churn' if predicted_value == 1 else 'not churn'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo display the results in our project, we can do so in a markdown cell. In this cell, we’ll use Jinja to provide the variables that we want to display on screen.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-markdown\"\u003E{% if predict %}\n\n#### The model is {{probability_of_prediction}}% confident that this user will {{prediction}}\n\n{% else %}\n\n#### No prediction has been made yet\n\n{% endif %}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EMaking Hex apps\u003C/h2\u003E\n","\u003Cp\u003EAt this stage of the project, we have completed building out our logic and are ready to share it with the world. To make the end product more user-friendly, we can use the app builder to simplify our logic. The app builder enables us to rearrange and organize the cells in our logic to hide the irrelevant parts and only show what matters.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/app.gif\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce you've arranged your cells and are satisfied with how it looks, use the share button to determine who can see this project and what level of access they have. Once shared, hit the publish button and your app will go live.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/share.gif\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations on making it to the end of this Lab where we explored churn modeling using Snowflake and Hex. We learned how to import/export data between Hex and Snowflake, train a Random Forest model, visualize predictions, convert a Hex project into a web app, and make predictions for new users. You can view the published version of this \u003Ca href=\"https://app.hex.tech/hex-public/app/8bd7b9bb-7f6c-41f1-9b4c-ff563a7fcaea/latest\"\u003Eproject here\u003C/a\u003E!\u003C/p\u003E\n","\u003Ch3\u003EWhat we've covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUse Snowflake’s “Partner Connect” to seamlessly create a Hex trial\u003C/li\u003E\u003Cli\u003EHow to navigate the Hex workspace/notebooks\u003C/li\u003E\u003Cli\u003EHow to train an Random forest model and deploy to Snowflake using UDFs\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://learn.hex.tech/docs\"\u003EHex docs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.Snowflake.com/en/\"\u003ESnowflake Docs\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Churn modeling using Snowflake and Hex","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\n\nIn this Quickstart guide, we will play the role of a data scientist at a telecom company that wants to identify users who are at high risk of churning. To accomplish this, we need to build a model that can learn how to identify such users. We will demonstrate how to use Hex in conjunction with Snowflake/Snowpark to build a Random Forest Classifier to help us with this task.\n\n### Prerequisites\n\n- Familiarity with basic Python and SQL\n- Familiarity with training ML models\n- Familiarity with data science notebooks\n- Go to the [Snowflake](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) sign-up page and register for a free account. After registration, you will receive an email containing a link that will take you to Snowflake, where you can sign in.\n\n### What You'll Learn\n\n- How to import/export data between Hex and Snowflake\n- How to train a Random Forest with Snowpark ML model\n- How to visualize the predicted results from the forecasting model\n- How to convert a Hex project into an interactive web app and make predictions on new users\n\n\n\n\n\u003C!-- ------------------------ --\u003E\n\n## Setting up partner connect\n\n\nAfter logging into your Snowflake account, you will land on the `Learn` page. To connect with Hex, navigate to the `Admin` tab on the left and click on `Partner connect`. In the search bar at the top, type `Hex` and the Hex partner connect tile will appear. Clicking on the tile will bring up a new screen, and click the `connect button` in the lower right corner. A new screen will confirm that your account has been created, from which you can click `Activate`.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/pc.gif)\n\n### Creating a workspace\n\nAfter activating your account, you'll be directed to Hex and prompted to create a new workspace and give it a name. Once you've named your workspace, you'll be taken to the projects page where you can create new projects, import existing projects (Hex or Jupyter), and navigate to other sections of your workspace.\n\n## Getting Started with Hex\n\n\nNow we can move back over to Hex and get started on our project. The first thing you'll need to do is get the Hex project that contains all of the code we'll work through to train our model.\n\nClicking this button will copy the template project into your new workspace.\n\n\u003Cbutton\u003E\n\n[Duplicate Hex project](https://app.hex.tech/new-project?signup=true&baseHexId=057d76c5-b7c3-465e-9e61-f939106e7c5d&baseOrgId=hex-public)\n\n\u003C/button\u003E\n\nNow that you've got your project imported, you will find yourself in the [Logic view](https://learn.hex.tech/docs/explore-data/projects/projects-introduction#the-notebook-view) of a Hex project. The Logic view is a notebook-like interface made up of cells such as code cells, markdown cells, input parameters and more! On the far left side, you'll see a control panel that will allow you to do things like upload files, import data connections, or search your project.\n\nBefore we dive into the code, we'll need to import our Snowflake data connection, which has been automatically created by the partner connect process.\n\nHead over to the Data sources tab represented by a database icon with a lightning bolt. You should see two data connections - [Demo] Hex public data and Snowflake. Import both connections.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/DC.gif)\n\nOne nice feature of Hex is the [reactive execution model](https://learn.hex.tech/docs/explore-data/projects/project-execution/execution-model#reactive-graph-based-execution-model). This means that when you run a cell, all related cells are also executed so that your projects are always in a clean state. However, if you want to ensure you don’t get ahead of yourself as we run through the tutorial, you can opt to turn this feature off. In the top right corner of your screen, you’ll see a Run mode dropdown. If this is set to Auto, select the dropdown and change it to cell only.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/mode.gif)\n\nIn the walkthrough video of the lab, we opt to leave this on and instead un-comment out the template code as we work through the project.\n\n## Reading and writing data\n\n\nTo predict customer churn, we first need data to train our model. In the SQL cell labeled **Pull churn results** assign `[Demo] Hex public data` as the data connection source and run the cell.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/pull_data.png)\n\nAt the bottom of this cell, you will see a green output variable labeled `data`. This is a Pandas dataframe, and we are going to write it back into our `Snowflake` data connection. To do so, input the following configurations to the writeback cell (labeled: **Writeback to snowflake)**\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/writeback.png)\n\n- **Source**: `data`\n- **Connection**: `Snowflake`\n- **Database**: `PC_HEX_DB`\n- **Schema**: `Public`\n- **Table**: _Static_ and name it `CHURN_DATA`\n\nOnce the config is set, enable `Logic session` as the writeback mode (in the upper right of the cell) and run the cell.\n\nIn the SQL cell labeled **Churn data**, change the data source to `Snowflake` and execute the cell. You will see a green output variable named data at the bottom.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/sf_churn.png)\n\n## Data preparation\n\n\nNow that we have our data in Hex, we want to make sure the it’s clean enough for our machine learning algorithm. To ensure this, we’ll first check for any null values.\n\n```python\ndata.isnull().sum()\n```\n\nNow that we have checked for null values, let's look at the distribution of each variable.\n\n```python\n# create a 15x5 figure\nplt.figure(figsize=(15, 5))\n\n# create a new plot for each variable in our dataframe\nfor i, (k, v) in enumerate(data.items(), 1):\n    plt.subplot(3, 5, i)  # create subplots\n    plt.hist(v, bins = 50)\n    plt.title(f'{k}')\n\nplt.tight_layout()\nplt.show();\n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/chart.png)\n\nThe charts' results allow us to visualize each variables distribution, which can help us identify early signs of skewness, among other things. We can see that all of our continuous variables follow a fairly normal distribution, and further transformations won't be necessary. The `DataUsage` column appears a little off because many customers use 0GB of data, but there are also a lot of users who didn't have data plans in the first place, so this isn't considered an anomaly.\n\n## Understanding churn rate\n\n\nIf you take a look at our visuals, you may notice that the churn chart looks a little odd. Specifically, it looks like there are a lot more users who haven’t churned than who have.\n\nWe take a closer look at this by visualizing in a chart cell.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/imbal.png)\nAs you can see, the majority of observations are in support of user who haven’t yet churned. Specifically, 85% of user haven’t churned while the other 15% has. In machine learning, a class imbalance such as this can cause issues when evaluating the model since it’s not getting equal attention from each class. In the next section we will go over a method to combat the imbalance problem.\n\n## Establishing a Snowpark connection\n\n\nNow, we can connect to our Snowflake connection that we imported earlier. To do this head over to the data sources tab on the left control panel to find your Snowflake connection. If you hover your mouse over the connection and click on the dropdown next to the `query` button and select `get Snowpark session`. This will create a new cell for us with all the code needed to spin up a Snowpark session.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/snowpark.gif)\n\nTo ensure we don't run into any problems down the line, paste in the following line at the bottom of the cell.\n\n```python\nsession.use_schema(\"PC_HEX_DB.PUBLIC\")\n```\n\n## Feature engineering\n\n\nIn order to predict the churn outcomes for customers not in our dataset, we’ll need to train a model that can identify users who are at risk of churning from the history of users who have. However, it was mentioned in the last section that there is an imbalance in the class distribution that will cause problems for our model if not handled properly. One way to handle this is to create new data points such that the classes balance out. This is also known as upsampling.\n\nFor this, we’ll be using the `SMOTE` algorithm from the `imblearn` package.\n\nFirst, we'll get our features— aka everything except the target column, Churn. We do this in a SQL cell, making use of the SELECT / EXCLUDE syntax in Snowflake! Uncomment + run the SQL cell called \"Features\".\n\nNow, we'll upsample. We could do this locally using SMOTE, but we want this entire workflow to run in Snowpark end-to-end, so we're going to create a stored procedure to do our upsampling.\n\nRun the code cell labeled **Upsampling the data**.\n\n```python\ndef upsample(\n    session: Session,\n    features_table_name: str,\n) -\u003E str:\n\n    import pandas as pd\n    import sklearn\n    from imblearn.over_sampling import SMOTE\n\n    features_table = session.table(features_table_name).to_pandas()\n    X = features_table.drop(columns=[\"Churn\"])\n    y = features_table[\"Churn\"]\n    upsampler = SMOTE(random_state=111)\n    r_X, r_y = upsampler.fit_resample(X, y)\n    upsampled_data = pd.concat([r_X, r_y], axis=1)\n    upsampled_data.reset_index(inplace=True)\n    upsampled_data.rename(columns={'index': 'INDEX'}, inplace=True)\n\n\n\n    upsampled_data_spdf = session.write_pandas(\n        upsampled_data,\n        table_name=f'{features_table_name}_SAMPLED',\n        auto_create_table=True,\n        table_type='', # creates a permanent table\n        overwrite=True,\n    )\n\n    return \"Success\"\n```\n\nNow we have created a function that will be our stored procedure. It will perform upsampling, and write the upsampled data back to a new Snowflake table called '{features_table_name}\\_SAMPLED'.\n\nNow we create + call the stored procedure, in 2 more python cells:\n\n```python\nsession.sql('CREATE OR REPLACE STAGE SNOWPARK_STAGE').collect()\n\nsession.sproc.register(\n    upsample,\n    name=\"upsample_data_with_SMOTE\",\n    stage_location='@SNOWPARK_STAGE',\n    is_permanent=True,\n    execute_as='caller',\n    packages=['imbalanced-learn==0.10.1', 'pandas', 'snowflake-snowpark-python==1.6.1', 'scikit-learn==1.2.2'],\n    replace=True,\n)\n```\n\n```python\n# # here we call the sproc to perform the upsampling\nsession.call('upsample_data_with_SMOTE', 'TELECOM_CHURN')\n```\n\nNow all that's left is to query the upsampled data from the table using another SQL cell, in this case the Features upsampled cell. If you prefer, you could change your stored procedure to return a Snowpark DataFrame directly rather than writing back a permanent table, but if you are going to be doing continued work on this dataset, writing a permanent table may make more sense.\n\nNow that we have balanced our dataset, we can prepare our model for training. The model we have chosen for this project is a Random Forest classifier. A random forest creates an ensemble of smaller models that all make predictions on the same data. The prediction with the most votes is the prediction the model chooses.\n\nRather than use a typical random forest object, we'll make use of Snowflake ML. Snowflake ML offers capabilities for data science and machine learning tasks within Snowflake. It provides estimators and transformers compatible with scikit-learn and xgboost, allowing users to build and train ML models directly on Snowflake data. This eliminates the need to move data or use stored procedures. It uses wrappers around scikit-learn and xgboost classes for training and inference, ensuring optimized performance and security within Snowflake.\n\nIn the cell labeled `Snowflake ML model preprocessing` we'll import Snowpark ML to further process our dataset to prepare it for our model.\n\n```python\nimport snowflake.ml.modeling.preprocessing as pp\nfrom snowflake.ml.modeling.ensemble import RandomForestClassifier\ndataset = features_upsampled\n\n# Get the list of column names from the dataset\nfeature_names_input = [c for c in dataset.columns if c != '\"Churn\"' and c != \"INDEX\"]\n\n\n# Initialize a StandardScaler object with input and output column names\nscaler = pp.StandardScaler(\n    input_cols=feature_names_input,\n    output_cols=feature_names_input\n)\n\n# Fit the scaler to the dataset\nscaler.fit(dataset)\n\n# Transform the dataset using the fitted scaler\nscaled_features = scaler.transform(dataset)\n\n# Define the target variable (label) column name\nlabel = ['\"Churn\"']\n\n# Define the output column name for the predicted label\noutput_label = [\"predicted_churn\"]\n\n# Split the scaled_features dataset into training and testing sets with an 80/20 ratio\ntraining, testing = scaled_features.random_split(weights=[0.8, 0.2], seed=111)\n```\n\n### Accepting Anaconda terms\n\nBefore we can train the model, we'll need to accept the Anaconda terms and conditions.\nTo do this, navigate back to Snowflake and click on your username in the top left corner. You'll see a section that will allow you to switch to the `ORGADMIN` role. Once switched over, navigate to the `Admin` tab and select `Billing & Terms`. From here, you will see a section that will allow you to accept the anaconda terms and conditions. Once this is done, you can head back over to Hex and run the cell that trains our model.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/vhol-accept-terms.gif)\n\n## Model training\n\n\nNow we can train our model. Run the cell labeled `Model training`.\n\n```python\n# Initialize a RandomForestClassifier object with input, label, and output column names\nmodel = RandomForestClassifier(\n    input_cols=feature_names_input,\n    label_cols=label,\n    output_cols=output_label,\n)\n\n# Train the RandomForestClassifier model using the training set\nmodel.fit(training)\n\n# Predict the target variable (churn) for the testing set using the trained model\nresults = model.predict(testing)\n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/train.png)\n\nIn the next section, we will look at how well our model performed as well as which features played the most important role when predicting the churn outcome.\n\n## Model evaluation and feature importance\n\n\nIn order to understand how well our model performs at identifying users at risk of churning, we’ll need to evaluate how well it does predicting churn outcomes. Specifically, we’ll be looking at the recall score, which tells us _of all the customers that will churn, how many can it identify._\n\nRun the code cell labeled **Evaluate model** on _accuracy and recall._\n\n```python\npredictions = results.to_pandas().sort_values(\"INDEX\")[['predicted_churn'.upper()]].astype(int).to_numpy().flatten()\nactual = testing.to_pandas().sort_values(\"INDEX\")[['Churn']].to_numpy().flatten()\n\naccuracy = round(accuracy_score(actual, predictions), 3)\nrecall = round(recall_score(actual, predictions), 3)\n```\n\nThis will calculate an accuracy and recall score for us which we'll display in a [single value cell](https://learn.hex.tech/docs/explore-data/cells/visualization-cells/single-value-cells#single-value-cell-configuration).\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/scores.png)\n\n### Feature importance\n\nNext, we want to understand which features were deemed most important by the model when making predictions. Lucky for us, our model keeps track of the most important features, and we can access them using the `feature_importances_` attribute. Since we're using Snowflake-ml, we'll need to extract the original `sklearn` object from our model. Then we can perform feature importance as usual.\n\n```python\nrf = model.to_sklearn()\nimportances = pd.DataFrame(\n    list(zip(features.columns, rf.feature_importances_)),\n    columns=[\"feature\", \"importance\"],\n)\n```\n\nLet’s visualize the most important features.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/important.png)\n\n## Predicting churn for a new user\n\n\nNow is the moment we've all been waiting for: predicting the churn outcome for a new user. In this section, you should see an array of input parameters already in the project. Each of these inputs allow you to adjust a different feature that goes into predicting customer churn, which will simulate a new user. But we’ll still need to pass this data to our model, so how can we do that?\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/input.png)\n\nEach input parameter has its own variable as its output, and these variables can be referenced in a Python cell. The model expects the inputs it receives to be in a specific order otherwise it will get confused about what the features mean. Keeping this in mind, execute the Python cell labeled **_Create the user vector_**.\n\n```python\ninputs = [\n\t    account_weeks,\n\t    1 if renewed_contract else 0, # This value is a bool and we need to convert to numbers\n\t    1 if has_data_plan else 0, # This value is a bool and we need to convert to numbers\n\t    data_usage,\n\t    customer_service_calls,\n\t    mins_per_month,\n\t    daytime_calls,\n\t    monthly_charge,\n\t    overage_fee,\n\t    roam_mins,\n]\n```\n\nThis creates a list where each element represents a feature that our model can understand. However, before our model can accept these features, we need to transform our array. To do this, we will convert our list into a numpy array and reshape it so that there is only one row and one column for all features.\n\n```python\nuser_vector = np.array(inputs).reshape(1, -1)\n```\n\nAs a last step, we’ll need to scale our features within the original range that was used during the training phase. We already have a scaler fit on our original data and we can use the same one to scale these features.\n\n```python\nuser_vector_scaled = scaler.transform(user_vector)\n```\n\nThe final cell should look like this:\n\n```python\n# get model inputs\nuser_vector = np.array([\n    account_weeks,\n    1 if renewed_contract else 0,\n    1 if has_data_plan else 0,\n    data_usage,\n    customer_service_calls,\n    mins_per_month,\n    daytime_calls,\n    monthly_charge,\n    overage_fee,\n    roam_mins,\n]).reshape(1,-1)\n\nuser_dataframe = pd.DataFrame(user_vector, columns=scaler.input_cols)\nuser_vector = scaler.transform(user_dataframe)\nuser_vector.columns = [column_name.replace('\"', \"\") for column_name in user_vector.columns]\nuser_vector = session.create_dataframe(user_vector)\n```\n\nWe are now ready to make predictions. In the last code cell labeled **_Make predictions and get results_**, we will pass our user vector to the model's predict function, which will output its prediction. We will also obtain the probability for that prediction, allowing us to say: **_\"The model is 65% confident that this user will churn.\"_**\n\n```python\npredicted_value = model.predict(user_vector).toPandas()[['predicted_churn'.upper()]].values.astype(int).flatten()\nuser_probability = model.predict_proba(user_vector).toPandas()\nprobability_of_prediction = max(user_probability[user_probability.columns[-2:]].values[0]) * 100\nprediction = 'churn' if predicted_value == 1 else 'not churn'\n```\n\nTo display the results in our project, we can do so in a markdown cell. In this cell, we’ll use Jinja to provide the variables that we want to display on screen.\n\n```markdown\n{% if predict %}\n\n#### The model is {{probability_of_prediction}}% confident that this user will {{prediction}}\n\n{% else %}\n\n#### No prediction has been made yet\n\n{% endif %}\n```\n\n## Making Hex apps\n\n\nAt this stage of the project, we have completed building out our logic and are ready to share it with the world. To make the end product more user-friendly, we can use the app builder to simplify our logic. The app builder enables us to rearrange and organize the cells in our logic to hide the irrelevant parts and only show what matters.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/app.gif)\n\nOnce you've arranged your cells and are satisfied with how it looks, use the share button to determine who can see this project and what level of access they have. Once shared, hit the publish button and your app will go live.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hex-churn-model/share.gif)\n\n## Conclusion And Resources\n\n\nCongratulations on making it to the end of this Lab where we explored churn modeling using Snowflake and Hex. We learned how to import/export data between Hex and Snowflake, train a Random Forest model, visualize predictions, convert a Hex project into a web app, and make predictions for new users. You can view the published version of this [project here](https://app.hex.tech/hex-public/app/8bd7b9bb-7f6c-41f1-9b4c-ff563a7fcaea/latest)!\n\n### What we've covered\n\n- Use Snowflake’s “Partner Connect” to seamlessly create a Hex trial\n- How to navigate the Hex workspace/notebooks\n- How to train an Random forest model and deploy to Snowflake using UDFs\n\n### Resources\n\n- [Hex docs](https://learn.hex.tech/docs)\n- [Snowflake Docs](https://docs.Snowflake.com/en/)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-4d9221beb6","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-103293c66c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-b6edf2d215","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-e4d131db63","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. 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