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dependencies) takes time away from what matters: exploring data and building models. \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-in-workspaces/notebooks-in-workspaces-overview\"\u003ESnowflake Notebooks in Workspaces\u003C/a\u003E removes that friction by providing a cell-based, interactive environment for Python and SQL that runs directly inside Snowflake. You get access to your data, scalable compute, and a curated package library without leaving the platform.\u003C/p\u003E\n","\u003Cp\u003EThis guide walks you through a realistic data science workflow using the \u003Ca href=\"https://scikit-learn.org/stable/datasets/toy_dataset.html#wine-recognition-dataset\"\u003EWine dataset\u003C/a\u003E — from loading data and writing SQL queries to producing visualizations and training a classification model, all inside a single Snowflake Notebook.\u003C/p\u003E\n","\u003Cp\u003EThe pipeline covers five sequential stages:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStep\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESection\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat you do\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESetup\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELoad the Wine dataset, write it to a Snowflake temp table\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EEDA with SQL\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClass balance, per-class aggregations, ranked queries\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EEDA with Python\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGrouped box plots, 13x13 correlation heatmap, pairplot\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EMachine Learning Modeling\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETrain/test split, PCA scores, Random Forest + cross-validation\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EPost-ML Analysis\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConfusion matrix, feature importances, ROC curves, learning curve\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBasic familiarity with Python and SQL.\u003C/li\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/cortex-code?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E. Sign up for a \u003Ca href=\"https://signup.snowflake.com/cortex-code?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003E30-day free trial\u003C/a\u003E if required.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECortex Code\u003C/a\u003E (optional)\u003C/strong\u003E — not required if you use the provided code snippets directly. Needed if you want to use the \u003Cstrong\u003EPrompt\u003C/strong\u003E sections to generate or extend the code interactively.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to load an in-memory Python dataset into a pandas DataFrame and reference it from SQL cells using Jinja templating.\u003C/li\u003E\u003Cli\u003EHow SQL cells return \u003Cstrong\u003ESnowpark pandas (snowpandas) DataFrames\u003C/strong\u003E by default in Container Runtime 2.6 or higher, and how to convert them to pandas with \u003Ccode\u003E.to_pandas()\u003C/code\u003E when needed.\u003C/li\u003E\u003Cli\u003EHow to produce publication-quality EDA visualizations (box plots, heatmaps, pairplots) inside a Notebook.\u003C/li\u003E\u003Cli\u003EHow to train and evaluate a Random Forest classifier with interactive \u003Ccode\u003Eipywidgets\u003C/code\u003E sliders for hyperparameters.\u003C/li\u003E\u003Cli\u003EHow to interpret post-training diagnostics: confusion matrices, feature importances, ROC curves, and learning curves.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/workspaces\"\u003ESnowflake Workspaces\u003C/a\u003E and a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-in-workspaces/notebooks-in-workspaces-compute-setup\"\u003ECompute Pool\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EThe \u003Ca href=\"https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline.ipynb\"\u003Egetting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline.ipynb\u003C/a\u003E notebook file from the \u003Ca href=\"https://github.com/Snowflake-Labs/snowflake-demo-notebooks\"\u003ESnowflake Demo Notebooks\u003C/a\u003E repo.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EAn end-to-end classification pipeline on the Wine dataset:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAn in-memory pandas DataFrame (\u003Ccode\u003Edf_snow\u003C/code\u003E) holding 178 samples and 13 chemical features, referenced directly from SQL cells via Jinja templating.\u003C/li\u003E\u003Cli\u003ESQL EDA queries revealing class balance, alcohol statistics, and top samples by flavanoid content.\u003C/li\u003E\u003Cli\u003EPython EDA charts including grouped box plots and a 13x13 correlation heatmap.\u003C/li\u003E\u003Cli\u003EA trained \u003Ccode\u003ERandomForestClassifier\u003C/code\u003E with interactive hyperparameter sliders, evaluated via 5-fold cross-validation.\u003C/li\u003E\u003Cli\u003EPost-ML diagnostic charts: confusion matrix, feature importances, ROC curves, and learning curve.\u003C/li\u003E\u003C/ul\u003E\n<!-- https://excalidraw.com/#json=pBuG3522Q2TPKjL59ep3l,Ka9AXVQl0-xrtxC5G6UVvQ -->\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/workflow-diagram.png?v=e378f781\" alt=\"EDA & ML Pipeline Workflow\"\u003E\n\u003Cem\u003EEDA & ML Pipeline in Snowflake Notebooks — Wine dataset classification workflow\u003C/em\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EImport the Notebook into Snowflake\u003C/h2\u003E\n","\u003Ch3\u003EStep 1 — Download the notebook\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EGo to the repo page with the \u003Ca href=\"https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline.ipynb\"\u003Egetting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline.ipynb\u003C/a\u003E notebook file, then click \u003Cstrong\u003EDownload raw file\u003C/strong\u003E (top-right icon).\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 2 — Import into Snowsight\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ELog in to \u003Ca href=\"https://app.snowflake.com\"\u003ESnowsight\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EProjects > Workspaces\u003C/strong\u003E in the left sidebar.\u003C/li\u003E\u003Cli\u003EIn the \u003Cstrong\u003EWorkspaces\u003C/strong\u003E tab on the left pane, click on \u003Cstrong\u003E+ Add new\u003C/strong\u003E, then \u003Cstrong\u003EUpload files\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect the \u003Ccode\u003E.ipynb\u003C/code\u003E notebook file from your local computer that you've already downloaded in step 1 and click \u003Cstrong\u003EOpen\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EFrom the \u003Cstrong\u003EWorkspaces\u003C/strong\u003E tab on the left pane, click on the notebook file to open it up. Next, click on the \u003Cstrong\u003E"Connect"\u003C/strong\u003E widget so that it connects to the compute service.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 3 — Switch to Container Runtime\u003C/h3\u003E\n","\u003Cp\u003EThis notebook uses packages such as \u003Ccode\u003Escikit-learn\u003C/code\u003E, \u003Ccode\u003Eseaborn\u003C/code\u003E, and \u003Ccode\u003Eipywidgets\u003C/code\u003E that are available on Container Runtime. This guide was developed and tested with \u003Cstrong\u003EContainer Runtime 2.6 (CPU)\u003C/strong\u003E.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen the notebook and in the top \u003Cstrong\u003EConnect/Connected\u003C/strong\u003E widget, click on the drop-down to create a new service or edit an existing service to use runtime version 2.6 or higher.\u003C/li\u003E\u003Cli\u003EClick on the \u003Cstrong\u003EConnect\u003C/strong\u003E widget to start the service and wait for the container to start (typically under 60 seconds).\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup: Load the Wine Dataset\u003C/h2\u003E\n","\u003Cp\u003EThe first section loads the scikit-learn Wine dataset into a pandas DataFrame, connects to Snowflake, and prepares a SQL-safe copy of the DataFrame called \u003Ccode\u003Edf_snow\u003C/code\u003E. SQL cells in the notebook reference \u003Ccode\u003Edf_snow\u003C/code\u003E directly via Jinja templating (\u003Ccode\u003E{{df_snow}}\u003C/code\u003E), so no explicit table upload is needed.\u003C/p\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n","\u003Cp\u003EUse this prompt with an AI coding assistant to extend this section:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ELoad the scikit-learn Wine dataset into a pandas DataFrame. Sanitise column names\nby replacing / with _ so they are safe to use in SQL. Print the dataset shape,\nfeature names, and class names.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELoad the Wine Dataset\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport re\nimport pandas as pd\nfrom sklearn.datasets import load_wine\n\n# Load Wine dataset into a pandas DataFrame\nwine = load_wine()\ndf = pd.DataFrame(wine.data, columns=wine.feature_names)\ndf['cultivar'] = wine.target\ndf['cultivar_name'] = df['cultivar'].map({0: 'Cultivar 0', 1: 'Cultivar 1', 2: 'Cultivar 2'})\n\nprint(f"Dataset shape: {df.shape}")\nprint(f"Features: {list(wine.feature_names)}")\nprint(f"Classes: {list(wine.target_names)}")\n\n# Sanitise column names for SQL (replace / with _)\ndef safe_col(name):\n    return re.sub(r'[^a-zA-Z0-9_]', '_', name)\n\ndf_snow = df.rename(columns={c: safe_col(c) for c in df.columns})\nprint(f"\\ndf_snow columns: {list(df_snow.columns)}")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Edf_snow\u003C/code\u003E DataFrame is identical to \u003Ccode\u003Edf\u003C/code\u003E except its column names replace \u003Ccode\u003E/\u003C/code\u003E with \u003Ccode\u003E_\u003C/code\u003E — required because the SQL Jinja templating syntax (\u003Ccode\u003E{{df_snow}}\u003C/code\u003E) does not accept slashes in column names.\u003C/p\u003E\n","\u003Ch3\u003EInstall Packages\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E! pip install ipywidgets\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Eipywidgets\u003C/code\u003E provides interactive sliders for the hyperparameter tuning section. It is not pre-installed on Container Runtime.\u003C/p\u003E\n","\u003Ch3\u003EConnect to Snowflake\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\nprint(f'Connected as : {session.get_current_user()}')\nprint(f'Role         : {session.get_current_role()}')\nprint(f'Warehouse    : {session.get_current_warehouse()}')\n\nresult = session.sql('SELECT CURRENT_TIMESTAMP() AS now, CURRENT_VERSION() AS sf_version').collect()\nfor row in result:\n    print(f'Timestamp : {row["NOW"]}')\n    print(f'SF version: {row["SF_VERSION"]}')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Eget_active_session()\u003C/code\u003E connects to the Snowflake session that is already attached to the running notebook — no credentials are required.\u003C/p\u003E\n","\u003Cp\u003EThe notebook does not explicitly write \u003Ccode\u003Edf_snow\u003C/code\u003E to \u003Ccode\u003EWINE_TMP\u003C/code\u003E in a separate cell; instead, SQL cells reference the DataFrame directly via Jinja templating (\u003Ccode\u003E{{df_snow}}\u003C/code\u003E), which Snowflake Notebooks evaluates at query time.\u003C/p\u003E\n","\u003Ch3\u003EWhat Gets Generated\u003C/h3\u003E\n","\u003Cp\u003ERunning this section prints the dataset dimensions, feature list, class names, and Snowflake session details:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EDataset shape: (178, 15)\nFeatures: ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium',\n           'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins',\n           'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\nClasses: ['class_0', 'class_1', 'class_2']\n\ndf_snow columns: ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium',\n                  'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins',\n                  'color_intensity', 'hue', 'od280_od315_of_diluted_wines', 'proline',\n                  'cultivar', 'cultivar_name']\n\nConnected as : JANE_DOE\nRole         : SYSADMIN\nWarehouse    : COMPUTE_WH\nTimestamp : 2026-06-19 10:00:00.000\nSF version: 8.x.x\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEDA with SQL\u003C/h2\u003E\n","\u003Cp\u003EWith \u003Ccode\u003Edf_snow\u003C/code\u003E in memory, SQL cells can reference it directly using the \u003Ccode\u003E{{df_snow}}\u003C/code\u003E Jinja syntax. Snowflake Notebooks evaluates the template at query time, serialises the DataFrame, and executes the query — all transparently.\u003C/p\u003E\n","\u003Cp\u003ESQL cells use the \u003Ccode\u003E%%sql\u003C/code\u003E cell magic. Adding \u003Ccode\u003E-r <variable_name>\u003C/code\u003E captures the result as a \u003Cstrong\u003ESnowpark pandas (snowpandas) DataFrame\u003C/strong\u003E for use in subsequent Python cells. In Container Runtime 2.6 and later, SQL cell results are returned as Snowpark pandas DataFrames by default — if a downstream operation requires a regular pandas DataFrame, call \u003Ccode\u003E.to_pandas()\u003C/code\u003E on the result:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E%%sql -r df_result\nSELECT ... FROM {{df_snow}}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Convert to pandas if needed for downstream pandas operations\ndf_result_pd = df_result.to_pandas()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n","\u003Cp\u003EUse this prompt with an AI coding assistant to extend this section with more advanced SQL patterns:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUsing a Snowpark session in a Snowflake Notebook, write four SQL cells that\nreference a pandas DataFrame via Jinja templating ({{df_snow}}): (1) count\nsamples per cultivar with percentage of total using a window function, (2)\ncompute a five-number summary (min, Q1, median, Q3, max) of alcohol content\ngrouped by cultivar using PERCENTILE_CONT, (3) rank the top 3 samples per\ncultivар by flavanoid content using RANK() OVER (PARTITION BY), and (4) compute\nper-feature average by cultivar using a single-scan UNPIVOT + PIVOT instead of\nmultiple UNION ALL subqueries.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EClass Distribution\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E%%sql -r df_class_dist\nSELECT\n    cultivar,\n    cultivar_name,\n    COUNT(*) AS sample_count\nFROM {{df_snow}}\nGROUP BY cultivar, cultivar_name\nORDER BY cultivar\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis query confirms whether the dataset is balanced across the three Wine cultivar classes (0, 1, 2).\u003C/p\u003E\n","\u003Ch3\u003EAlcohol Stats per Cultivar\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E%%sql -r df_alcohol_stats\nSELECT\n    cultivar_name,\n    ROUND(MIN(alcohol), 3)  AS min_alcohol,\n    ROUND(AVG(alcohol), 3)  AS avg_alcohol,\n    ROUND(MAX(alcohol), 3)  AS max_alcohol\nFROM {{df_snow}}\nGROUP BY cultivar_name\nORDER BY cultivar_name\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E%%sql -r <variable>\u003C/code\u003E magic captures the result into a Python variable (\u003Ccode\u003Edf_alcohol_stats\u003C/code\u003E) for downstream use in Python cells.\u003C/p\u003E\n","\u003Ch3\u003ETop Samples by Flavanoid Content\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E%%sql -r df_top_flavanoids\nSELECT\n    cultivar_name,\n    ROUND(alcohol, 3)    AS alcohol,\n    ROUND(flavanoids, 3) AS flavanoids,\n    ROUND(proline, 0)    AS proline\nFROM {{df_snow}}\nORDER BY flavanoids DESC\nLIMIT 9\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAverage Feature Values per Cultivar\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E%%sql -r df_feature_avgs\nSELECT\n    cultivar_name,\n    ROUND(AVG(alcohol), 3)         AS avg_alcohol,\n    ROUND(AVG(flavanoids), 3)      AS avg_flavanoids,\n    ROUND(AVG(color_intensity), 3) AS avg_color_intensity,\n    ROUND(AVG(proline), 3)         AS avg_proline\nFROM {{df_snow}}\nGROUP BY cultivar_name\nORDER BY cultivar_name\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis reveals how the three cultivars differ on the features most commonly used in Wine classification tasks.\u003C/p\u003E\n","\u003Ch3\u003EWhat Gets Generated\u003C/h3\u003E\n","\u003Cp\u003EEach SQL cell returns a result table rendered inline in the notebook. For example, the class distribution query returns:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E  cultivar  cultivar_name  sample_count\n0        0    Cultivar 0            59\n1        1    Cultivar 1            71\n2        2    Cultivar 2            48\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAnd the alcohol stats query returns:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E  cultivar_name  min_alcohol  avg_alcohol  max_alcohol\n0   Cultivar 0        11.45       13.745        14.83\n1   Cultivar 1        11.03       12.279        14.10\n2   Cultivar 2        11.03       13.153        14.34\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEDA with Python\u003C/h2\u003E\n","\u003Cp\u003EPython-based EDA focuses on the \u003Cem\u003Eshape\u003C/em\u003E of the data — how features are distributed across cultivar classes and how strongly they correlate with each other.\u003C/p\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n","\u003Cp\u003EUse this prompt with an AI coding assistant to extend this section:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUsing matplotlib and seaborn, produce three visualisations for the Wine dataset:\n(1) a grid of grouped box plots showing the distribution of every feature broken\nout by cultivar, (2) a lower-triangle 13x13 Pearson correlation heatmap with\nannotated coefficients, and (3) a pairplot of the five most discriminative\nfeatures coloured by cultivar class.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGrouped Box Plots\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport matplotlib.pyplot as plt\n\nfeatures = wine.feature_names\nn_cols = 4\nn_rows = (len(features) + n_cols - 1) // n_cols\n\nfig, axes = plt.subplots(n_rows, n_cols, figsize=(18, n_rows * 3.5))\naxes = axes.flatten()\ncolors = ['#29B5E8', '#FF6B35', '#4CAF50']\n\nfor i, feat in enumerate(features):\n    ax = axes[i]\n    data_by_class = [df[df['cultivar'] == c][feat].values for c in [0, 1, 2]]\n    bp = ax.boxplot(data_by_class, patch_artist=True, tick_labels=['C0', 'C1', 'C2'],\n                    medianprops=dict(color='black', linewidth=1.5))\n    for patch, color in zip(bp['boxes'], colors):\n        patch.set_facecolor(color)\n        patch.set_alpha(0.7)\n    ax.set_title(feat, fontsize=9, fontweight='bold')\n    ax.grid(True, alpha=0.3, axis='y')\n\nfor j in range(len(features), len(axes)):\n    axes[j].set_visible(False)\n\nfig.suptitle('Feature Distributions by Cultivar (Grouped Box Plots)', fontsize=14, fontweight='bold')\nplt.tight_layout()\nplt.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe 4x4 grid of box plots shows how each of the 13 chemical features is distributed across the three cultivar classes. Features such as \u003Cstrong\u003Eflavanoids\u003C/strong\u003E and \u003Cstrong\u003Eproline\u003C/strong\u003E show strong class separation — they are good candidates for classification.\u003C/p\u003E\n","\u003Ch3\u003ECorrelation Heatmap\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport numpy as np\nimport seaborn as sns\n\nnumeric_df = df[list(wine.feature_names)]\ncorr = numeric_df.corr()\n\nfig, ax = plt.subplots(figsize=(13, 11))\nsns.heatmap(\n    corr,\n    annot=True, fmt='.2f',\n    cmap='coolwarm', center=0,\n    linewidths=0.4,\n    annot_kws={'size': 7},\n    ax=ax\n)\nax.set_title('Feature Correlation Matrix (13x13)', fontsize=14, fontweight='bold')\nplt.tight_layout()\nplt.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe 13x13 heatmap annotates every Pearson correlation coefficient. Notable strong correlations include \u003Cstrong\u003Eflavanoids\u003C/strong\u003E and \u003Cstrong\u003Etotal_phenols\u003C/strong\u003E (r ≈ 0.86) — meaning these features carry similar information and one could be dropped to reduce multicollinearity before modeling.\u003C/p\u003E\n","\u003Ch3\u003EDescriptive Statistics\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Estats = df[list(wine.feature_names)].describe().T.round(3)\nprint(stats.to_string())\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis prints count, mean, std, min, 25th/50th/75th percentile, and max for all 13 features in a single transposed table — useful for spotting scale differences before applying \u003Ccode\u003EStandardScaler\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EPairplot — Key Features by Cultivar\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ekey_features = ['alcohol', 'flavanoids', 'color_intensity', 'proline',\n                'od280/od315_of_diluted_wines']\npair_df = df[key_features + ['cultivar_name']].copy()\n\npalette = {'Cultivar 0': '#29B5E8', 'Cultivar 1': '#FF6B35', 'Cultivar 2': '#4CAF50'}\ng = sns.pairplot(pair_df, hue='cultivar_name', palette=palette,\n                 plot_kws={'alpha': 0.6, 's': 25}, diag_kind='kde')\ng.figure.suptitle('Pairplot — Key Features by Cultivar', y=1.02, fontsize=13, fontweight='bold')\nplt.tight_layout()\nplt.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe pairplot of the 5 most discriminative features shows near-linear separability between cultivar classes in 2D projections — a strong signal that a linear or tree-based classifier should achieve high accuracy.\u003C/p\u003E\n","\u003Ch3\u003EWhat Gets Generated\u003C/h3\u003E\n","\u003Cp\u003EThree figures are rendered inline in the notebook:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGrouped box plots\u003C/strong\u003E — a 4x4 grid showing the distribution of all 13 features split by cultivar class. Features like \u003Ccode\u003Eflavanoids\u003C/code\u003E and \u003Ccode\u003Eproline\u003C/code\u003E show clean separation between classes:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/grouped_box_plots.png?v=e378f781\" alt=\"Grouped Box Plots by Cultivar\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECorrelation heatmap\u003C/strong\u003E — a 13x13 annotated Pearson correlation matrix. Strong positive correlations appear between \u003Ccode\u003Eflavanoids\u003C/code\u003E and \u003Ccode\u003Etotal_phenols\u003C/code\u003E (r ≈ 0.86):\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/correlation_heatmap.png?v=e378f781\" alt=\"Feature Correlation Matrix\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPairplot\u003C/strong\u003E — scatter matrix of the 5 most discriminative features coloured by cultivar, showing near-linear separability:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/pairplot_key_features.png?v=e378f781\" alt=\"Pairplot Key Features by Cultivar\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMachine Learning Modeling\u003C/h2\u003E\n","\u003Cp\u003EThis section preprocesses the data, visualizes the train/test split in PCA space, exposes interactive hyperparameter sliders, trains a Random Forest, and evaluates it with cross-validation.\u003C/p\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n","\u003Cp\u003EUse this prompt with an AI coding assistant to extend this section:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESplit the Wine dataset 80/20 with stratification and scale features using\nStandardScaler. Fit a PCA with 2 components and plot the scores coloured by\n(a) train/test split and (b) cultivar class in side-by-side scatter plots. Add\nipywidgets IntSlider widgets for n_estimators (range 10-500, step 10) and\nmax_depth (range 1-20), then train a RandomForestClassifier reading those slider\nvalues, report test-set accuracy, and run 5-fold cross-validation on the full\ndataset.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPreprocessing: Train/Test Split and Scaling\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\n\nX = df[list(wine.feature_names)].values\ny = df['cultivar'].values\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.2, random_state=42, stratify=y\n)\n\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\n\nprint(f"Train set: {X_train_scaled.shape[0]} samples")\nprint(f"Test set:  {X_test_scaled.shape[0]} samples")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAn 80/20 stratified split is used so that the class proportions are preserved in both train and test sets. \u003Ccode\u003EStandardScaler\u003C/code\u003E is \u003Cstrong\u003Efit only on the training set\u003C/strong\u003E to avoid data leakage — it is then applied to the test set using the training-set statistics.\u003C/p\u003E\n","\u003Ch3\u003EPCA Scores Panel Plot\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom sklearn.decomposition import PCA\n\nX_full = df[list(wine.feature_names)].values\nX_full_scaled = scaler.transform(X_full)\n\npca = PCA(n_components=2, random_state=42)\nX_pca = pca.fit_transform(X_full_scaled)\nvar_explained = pca.explained_variance_ratio_ * 100\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe PCA scores plot has two panels:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ELeft\u003C/strong\u003E: train samples (blue) and test samples (orange) overlaid in 2D PCA space — confirming the split is representative and not accidentally grouped in one region.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERight\u003C/strong\u003E: the same points coloured by cultivar class — confirming that the three classes are largely linearly separable in the first two principal components.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EInteractive Hyperparameter Sliders\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport ipywidgets as widgets\nfrom IPython.display import display\n\nn_estimators_slider = widgets.IntSlider(\n    value=100, min=10, max=500, step=10,\n    description='n_estimators:',\n    style={'description_width': 'initial'},\n    continuous_update=False\n)\n\nmax_depth_slider = widgets.IntSlider(\n    value=5, min=1, max=20, step=1,\n    description='max_depth:',\n    style={'description_width': 'initial'},\n    continuous_update=False\n)\n\nprint('Adjust sliders then run the next cell to train the model.')\ndisplay(n_estimators_slider, max_depth_slider)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAdjust the sliders, then run the next cell. The model will be retrained with the new values each time you run it.\u003C/p\u003E\n","\u003Ch3\u003ETrain Random Forest and Cross-Validate\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import cross_val_score\n\nn_estimators = n_estimators_slider.value\nmax_depth = max_depth_slider.value\n\nrf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42)\nrf.fit(X_train_scaled, y_train)\n\ntest_accuracy = rf.score(X_test_scaled, y_test)\nprint(f"Test set accuracy: {test_accuracy:.4f} ({test_accuracy*100:.1f}%)")\n\ncv_scores = cross_val_score(rf, scaler.transform(X), y, cv=5, scoring='accuracy')\nprint(f"\\n5-Fold Cross-Validation:")\nprint(f"  Scores: {[f'{s:.3f}' for s in cv_scores]}")\nprint(f"  Mean:   {cv_scores.mean():.4f} +/- {cv_scores.std():.4f}")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EClassification Report\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom sklearn.metrics import classification_report\n\ny_pred = rf.predict(X_test_scaled)\nprint(classification_report(y_test, y_pred, target_names=wine.target_names))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe per-class precision, recall, and F1-score confirm which cultivar classes (if any) are harder for the model to distinguish.\u003C/p\u003E\n","\u003Ch3\u003EWhat Gets Generated\u003C/h3\u003E\n","\u003Cp\u003EThe PCA scores panel confirms the split is representative and that cultivars are linearly separable in 2D PCA space:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/pca_scores_panel.png?v=e378f781\" alt=\"PCA Scores Panel Plot\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe Random Forest training cell prints accuracy and cross-validation scores:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ETraining RandomForest with n_estimators=100, max_depth=5\nTest set accuracy: 0.9722 (97.2%)\n\n5-Fold Cross-Validation:\n  Scores: ['0.944', '0.944', '1.000', '1.000', '0.971']\n  Mean:   0.9722 +/- 0.0249\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPost-ML Analysis\u003C/h2\u003E\n","\u003Cp\u003EPost-training diagnostics help you understand where the model makes mistakes, which features drive its predictions, how well it separates classes across all decision thresholds, and whether additional training data would improve performance.\u003C/p\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n","\u003Cp\u003EUse this prompt with an AI coding assistant to extend this section:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAfter training a Random Forest on the Wine dataset, produce four evaluation\nplots: (1) a seaborn heatmap confusion matrix for the test set, (2) a horizontal\nbar chart of feature importances sorted ascending, (3) one-vs-rest ROC curves\nwith AUC scores for all three cultivar classes on a single axes, and (4) a\nlearning curve showing mean training and cross-validation accuracy with +/-1 std\nshading as training set size increases. The learning curve title should reflect\nthe current n_estimators and max_depth values from the ipywidgets sliders.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfusion Matrix\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom sklearn.metrics import confusion_matrix\n\ncm = confusion_matrix(y_test, y_pred)\n\nfig, ax = plt.subplots(figsize=(6, 5))\nsns.heatmap(\n    cm, annot=True, fmt='d', cmap='Blues',\n    xticklabels=wine.target_names,\n    yticklabels=wine.target_names,\n    ax=ax\n)\nax.set_title('Confusion Matrix', fontsize=13, fontweight='bold')\nax.set_xlabel('Predicted Label')\nax.set_ylabel('True Label')\nplt.tight_layout()\nplt.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEach cell shows the count of test samples with a given true label (row) and predicted label (column). Off-diagonal cells represent misclassifications.\u003C/p\u003E\n","\u003Ch3\u003EFeature Importances\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimportances = rf.feature_importances_\nsorted_idx = np.argsort(importances)\n\nfig, ax = plt.subplots(figsize=(8, 7))\nax.barh(range(len(sorted_idx)), importances[sorted_idx], color='#29B5E8', alpha=0.8)\nax.set_yticks(range(len(sorted_idx)))\nax.set_yticklabels([wine.feature_names[i] for i in sorted_idx])\nax.set_title('Random Forest Feature Importances', fontsize=13, fontweight='bold')\nplt.tight_layout()\nplt.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFeature importances are measured by \u003Cstrong\u003Emean decrease in impurity\u003C/strong\u003E across all trees. Typically, \u003Cstrong\u003Eproline\u003C/strong\u003E, \u003Cstrong\u003Ecolor_intensity\u003C/strong\u003E, and \u003Cstrong\u003Eflavanoids\u003C/strong\u003E rank highest for the Wine dataset — consistent with the EDA observations.\u003C/p\u003E\n","\u003Ch3\u003EROC Curves (One-vs-Rest)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom sklearn.preprocessing import label_binarize\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.model_selection import cross_val_predict\n\ny_bin = label_binarize(y, classes=[0, 1, 2])\nX_scaled_all = scaler.transform(X)\n_, X_test_all, _, y_test_bin = train_test_split(\n    X_scaled_all, y_bin, test_size=0.2, random_state=42, stratify=y\n)\ny_score = rf.predict_proba(X_test_scaled)\n\ny_cv_score = cross_val_predict(\n    RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42),\n    X_scaled_all, y, cv=5, method='predict_proba'\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETwo ROC panels are plotted side by side:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ELeft\u003C/strong\u003E — AUC on the held-out test set (20% of data).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERight\u003C/strong\u003E — AUC from 5-fold CV out-of-fold predictions (all 178 samples used for evaluation without data leakage).\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EComparing the two panels lets you check whether strong test-set AUC is reproducible across multiple splits or is a lucky artifact of the particular 80/20 split.\u003C/p\u003E\n","\u003Ch3\u003ELearning Curve\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom sklearn.model_selection import learning_curve\n\ntrain_sizes, train_scores, val_scores = learning_curve(\n    RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42),\n    scaler.transform(X), y,\n    cv=5, scoring='accuracy',\n    train_sizes=np.linspace(0.1, 1.0, 10),\n    n_jobs=-1\n)\n\ntrain_mean = train_scores.mean(axis=1)\nval_mean   = val_scores.mean(axis=1)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe learning curve plots training accuracy and CV accuracy as a function of training set size. A small gap between the two curves at the rightmost point indicates the model is not overfitting and is unlikely to benefit significantly from collecting more data.\u003C/p\u003E\n","\u003Ch3\u003EWhat Gets Generated\u003C/h3\u003E\n","\u003Cp\u003EFour diagnostic plots are rendered inline:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConfusion matrix\u003C/strong\u003E — true vs predicted labels on the test set. Diagonal cells are correctly classified samples; off-diagonal cells are misclassifications:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/confusion_matrix.png?v=e378f781\" alt=\"Confusion Matrix\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFeature importances\u003C/strong\u003E — horizontal bar chart ranked by mean decrease in impurity. \u003Ccode\u003Eproline\u003C/code\u003E, \u003Ccode\u003Ecolor_intensity\u003C/code\u003E, and \u003Ccode\u003Eflavanoids\u003C/code\u003E are the top predictors:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/feature_importances.png?v=e378f781\" alt=\"Random Forest Feature Importances\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EROC curves\u003C/strong\u003E — one-vs-rest AUC side by side for the test set and 5-fold CV out-of-fold predictions. All three cultivars achieve AUC > 0.99:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/roc_curves.png?v=e378f781\" alt=\"ROC Curves One-vs-Rest\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELearning curve\u003C/strong\u003E — training and CV accuracy vs dataset size with ±1 std shading. The narrow gap at the right indicates no significant overfitting:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/learning_curve.png?v=e378f781\" alt=\"Learning Curve\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESummary and Next Steps\u003C/h2\u003E\n","\u003Ch3\u003EWhat You Built\u003C/h3\u003E\n","\u003Cp\u003EIn this guide you built a complete end-to-end classification pipeline inside a single Snowflake Notebook:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESetup\u003C/strong\u003E — loaded the Wine dataset into a pandas DataFrame, connected to Snowflake via \u003Ccode\u003Eget_active_session()\u003C/code\u003E, and sanitised column names for SQL compatibility.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESQL EDA\u003C/strong\u003E — queried the in-memory pandas DataFrame directly from SQL cells using \u003Ccode\u003E{{df_snow}}\u003C/code\u003E Jinja templating to verify class balance, compare alcohol statistics, surface top flavanoid samples, and compare per-cultivar feature averages. SQL cell results are returned as Snowpark pandas (snowpandas) DataFrames (call \u003Ccode\u003E.to_pandas()\u003C/code\u003E for downstream pandas operations).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPython EDA\u003C/strong\u003E — grouped box plots revealed per-feature class separability; the 13x13 correlation heatmap identified collinear features; a pairplot of the five most discriminative features confirmed near-linear class separability.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPCA scores plot\u003C/strong\u003E — confirmed the stratified 80/20 train/test split is representative and that cultivars are largely separable in 2D PCA space.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERandom Forest\u003C/strong\u003E — trained with interactive \u003Ccode\u003Eipywidgets\u003C/code\u003E sliders for \u003Ccode\u003En_estimators\u003C/code\u003E and \u003Ccode\u003Emax_depth\u003C/code\u003E; validated with 5-fold cross-validation to confirm the result generalizes beyond the single split.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPost-ML analysis\u003C/strong\u003E — confusion matrix identified which cultivars are misclassified; feature importances ranked proline, color_intensity, and flavanoids as the top predictors; ROC curves confirmed strong one-vs-rest AUC; the learning curve showed no significant overfitting.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETry other classifiers\u003C/strong\u003E — swap in \u003Ccode\u003ESVC\u003C/code\u003E, \u003Ccode\u003EGradientBoostingClassifier\u003C/code\u003E, or \u003Ccode\u003ELogisticRegression\u003C/code\u003E in place of \u003Ccode\u003ERandomForestClassifier\u003C/code\u003E to compare performance.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EHyperparameter search\u003C/strong\u003E — replace the manual sliders with \u003Ccode\u003EGridSearchCV\u003C/code\u003E or \u003Ccode\u003ERandomizedSearchCV\u003C/code\u003E from scikit-learn.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERegister the model\u003C/strong\u003E — use the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview\"\u003ESnowflake Model Registry\u003C/a\u003E to version, log metrics for, and deploy the trained model.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchedule the notebook\u003C/strong\u003E — use \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-schedule\"\u003ENotebook Scheduling\u003C/a\u003E to retrain the model on a cadence as new data arrives.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUse a real dataset\u003C/strong\u003E — replace the Wine dataset with your own table in Snowflake by reading it with \u003Ccode\u003Esession.table()\u003C/code\u003E or \u003Ccode\u003Esession.sql()\u003C/code\u003E instead of \u003Ccode\u003Eload_wine()\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-in-workspaces/notebooks-in-workspaces-overview\"\u003ESnowflake Notebooks in Workspaces\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview\"\u003ESnowflake Model Registry\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/index\"\u003ESnowpark Python API\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://scikit-learn.org/stable/datasets/toy_dataset.html#wine-recognition-dataset\"\u003Escikit-learn Wine dataset\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Getting Started with Snowflake Notebooks in Workspaces: Build an EDA and ML Pipeline","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nAs a data scientist, setting up a local environment for each new project (*i.e.* installing packages, configuring database connections, and managing dependencies) takes time away from what matters: exploring data and building models. [Snowflake Notebooks in Workspaces](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-in-workspaces/notebooks-in-workspaces-overview) removes that friction by providing a cell-based, interactive environment for Python and SQL that runs directly inside Snowflake. You get access to your data, scalable compute, and a curated package library without leaving the platform.\n\nThis guide walks you through a realistic data science workflow using the [Wine dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#wine-recognition-dataset) — from loading data and writing SQL queries to producing visualizations and training a classification model, all inside a single Snowflake Notebook.\n\nThe pipeline covers five sequential stages:\n\n| Step | Section | What you do |\n|------|---------|-------------|\n| 1 | **Setup** | Load the Wine dataset, write it to a Snowflake temp table |\n| 2 | **EDA with SQL** | Class balance, per-class aggregations, ranked queries |\n| 3 | **EDA with Python** | Grouped box plots, 13x13 correlation heatmap, pairplot |\n| 4 | **Machine Learning Modeling** | Train/test split, PCA scores, Random Forest + cross-validation |\n| 5 | **Post-ML Analysis** | Confusion matrix, feature importances, ROC curves, learning curve |\n\n### Prerequisites\n\n- Basic familiarity with Python and SQL.\n- A [Snowflake account](https://signup.snowflake.com/cortex-code?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides). Sign up for a [30-day free trial](https://signup.snowflake.com/cortex-code?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) if required.\n- **[Cortex Code](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) (optional)** — not required if you use the provided code snippets directly. Needed if you want to use the **Prompt** sections to generate or extend the code interactively.\n\n### What You'll Learn\n\n- How to load an in-memory Python dataset into a pandas DataFrame and reference it from SQL cells using Jinja templating.\n- How SQL cells return **Snowpark pandas (snowpandas) DataFrames** by default in Container Runtime 2.6 or higher, and how to convert them to pandas with `.to_pandas()` when needed.\n- How to produce publication-quality EDA visualizations (box plots, heatmaps, pairplots) inside a Notebook.\n- How to train and evaluate a Random Forest classifier with interactive `ipywidgets` sliders for hyperparameters.\n- How to interpret post-training diagnostics: confusion matrices, feature importances, ROC curves, and learning curves.\n\n### What You'll Need\n\n- Access to [Snowflake Workspaces](https://docs.snowflake.com/en/user-guide/ui-snowsight/workspaces) and a [Compute Pool](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-in-workspaces/notebooks-in-workspaces-compute-setup).\n- The [getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline.ipynb](https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline.ipynb) notebook file from the [Snowflake Demo Notebooks](https://github.com/Snowflake-Labs/snowflake-demo-notebooks) repo.\n\n### What You'll Build\n\nAn end-to-end classification pipeline on the Wine dataset:\n\n- An in-memory pandas DataFrame (`df_snow`) holding 178 samples and 13 chemical features, referenced directly from SQL cells via Jinja templating.\n- SQL EDA queries revealing class balance, alcohol statistics, and top samples by flavanoid content.\n- Python EDA charts including grouped box plots and a 13x13 correlation heatmap.\n- A trained `RandomForestClassifier` with interactive hyperparameter sliders, evaluated via 5-fold cross-validation.\n- Post-ML diagnostic charts: confusion matrix, feature importances, ROC curves, and learning curve.\n\n\u003C!-- https://excalidraw.com/#json=pBuG3522Q2TPKjL59ep3l,Ka9AXVQl0-xrtxC5G6UVvQ --\u003E\n![EDA & ML Pipeline Workflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/workflow-diagram.png?v=e378f781)\n*EDA & ML Pipeline in Snowflake Notebooks — Wine dataset classification workflow*\n\n\u003C!-- ------------------------ --\u003E\n## Import the Notebook into Snowflake\n\n### Step 1 — Download the notebook\n\n- Go to the repo page with the [getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline.ipynb](https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline.ipynb) notebook file, then click **Download raw file** (top-right icon).\n\n### Step 2 — Import into Snowsight\n\n1. Log in to [Snowsight](https://app.snowflake.com).\n2. Navigate to **Projects \u003E Workspaces** in the left sidebar.\n3. In the **Workspaces** tab on the left pane, click on **+ Add new**, then **Upload files**. \n4. Select the `.ipynb` notebook file from your local computer that you've already downloaded in step 1 and click **Open**.\n5. From the **Workspaces** tab on the left pane, click on the notebook file to open it up. Next, click on the **\"Connect\"** widget so that it connects to the compute service.\n\n### Step 3 — Switch to Container Runtime\n\nThis notebook uses packages such as `scikit-learn`, `seaborn`, and `ipywidgets` that are available on Container Runtime. This guide was developed and tested with **Container Runtime 2.6 (CPU)**.\n\n1. Open the notebook and in the top **Connect/Connected** widget, click on the drop-down to create a new service or edit an existing service to use runtime version 2.6 or higher.\n2. Click on the **Connect** widget to start the service and wait for the container to start (typically under 60 seconds).\n\n\n\u003C!-- ------------------------ --\u003E\n## Setup: Load the Wine Dataset\n\nThe first section loads the scikit-learn Wine dataset into a pandas DataFrame, connects to Snowflake, and prepares a SQL-safe copy of the DataFrame called `df_snow`. SQL cells in the notebook reference `df_snow` directly via Jinja templating (`{{df_snow}}`), so no explicit table upload is needed.\n\n### Prompt\n\nUse this prompt with an AI coding assistant to extend this section:\n\n```\nLoad the scikit-learn Wine dataset into a pandas DataFrame. Sanitise column names\nby replacing / with _ so they are safe to use in SQL. Print the dataset shape,\nfeature names, and class names.\n```\n\n### Load the Wine Dataset\n\n```python\nimport re\nimport pandas as pd\nfrom sklearn.datasets import load_wine\n\n# Load Wine dataset into a pandas DataFrame\nwine = load_wine()\ndf = pd.DataFrame(wine.data, columns=wine.feature_names)\ndf['cultivar'] = wine.target\ndf['cultivar_name'] = df['cultivar'].map({0: 'Cultivar 0', 1: 'Cultivar 1', 2: 'Cultivar 2'})\n\nprint(f\"Dataset shape: {df.shape}\")\nprint(f\"Features: {list(wine.feature_names)}\")\nprint(f\"Classes: {list(wine.target_names)}\")\n\n# Sanitise column names for SQL (replace / with _)\ndef safe_col(name):\n    return re.sub(r'[^a-zA-Z0-9_]', '_', name)\n\ndf_snow = df.rename(columns={c: safe_col(c) for c in df.columns})\nprint(f\"\\ndf_snow columns: {list(df_snow.columns)}\")\n```\n\nThe `df_snow` DataFrame is identical to `df` except its column names replace `/` with `_` — required because the SQL Jinja templating syntax (`{{df_snow}}`) does not accept slashes in column names.\n\n### Install Packages\n\n```python\n! pip install ipywidgets\n```\n\n`ipywidgets` provides interactive sliders for the hyperparameter tuning section. It is not pre-installed on Container Runtime.\n\n### Connect to Snowflake\n\n```python\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\nprint(f'Connected as : {session.get_current_user()}')\nprint(f'Role         : {session.get_current_role()}')\nprint(f'Warehouse    : {session.get_current_warehouse()}')\n\nresult = session.sql('SELECT CURRENT_TIMESTAMP() AS now, CURRENT_VERSION() AS sf_version').collect()\nfor row in result:\n    print(f'Timestamp : {row[\"NOW\"]}')\n    print(f'SF version: {row[\"SF_VERSION\"]}')\n```\n\n`get_active_session()` connects to the Snowflake session that is already attached to the running notebook — no credentials are required.\n\nThe notebook does not explicitly write `df_snow` to `WINE_TMP` in a separate cell; instead, SQL cells reference the DataFrame directly via Jinja templating (`{{df_snow}}`), which Snowflake Notebooks evaluates at query time.\n\n### What Gets Generated\n\nRunning this section prints the dataset dimensions, feature list, class names, and Snowflake session details:\n\n```\nDataset shape: (178, 15)\nFeatures: ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium',\n           'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins',\n           'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\nClasses: ['class_0', 'class_1', 'class_2']\n\ndf_snow columns: ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium',\n                  'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins',\n                  'color_intensity', 'hue', 'od280_od315_of_diluted_wines', 'proline',\n                  'cultivar', 'cultivar_name']\n\nConnected as : JANE_DOE\nRole         : SYSADMIN\nWarehouse    : COMPUTE_WH\nTimestamp : 2026-06-19 10:00:00.000\nSF version: 8.x.x\n```\n\n\u003C!-- ------------------------ --\u003E\n## EDA with SQL\n\nWith `df_snow` in memory, SQL cells can reference it directly using the `{{df_snow}}` Jinja syntax. Snowflake Notebooks evaluates the template at query time, serialises the DataFrame, and executes the query — all transparently.\n\nSQL cells use the `%%sql` cell magic. Adding `-r \u003Cvariable_name\u003E` captures the result as a **Snowpark pandas (snowpandas) DataFrame** for use in subsequent Python cells. In Container Runtime 2.6 and later, SQL cell results are returned as Snowpark pandas DataFrames by default — if a downstream operation requires a regular pandas DataFrame, call `.to_pandas()` on the result:\n\n```\n%%sql -r df_result\nSELECT ... FROM {{df_snow}}\n```\n\n```python\n# Convert to pandas if needed for downstream pandas operations\ndf_result_pd = df_result.to_pandas()\n```\n\n### Prompt\n\nUse this prompt with an AI coding assistant to extend this section with more advanced SQL patterns:\n\n```\nUsing a Snowpark session in a Snowflake Notebook, write four SQL cells that\nreference a pandas DataFrame via Jinja templating ({{df_snow}}): (1) count\nsamples per cultivar with percentage of total using a window function, (2)\ncompute a five-number summary (min, Q1, median, Q3, max) of alcohol content\ngrouped by cultivar using PERCENTILE_CONT, (3) rank the top 3 samples per\ncultivар by flavanoid content using RANK() OVER (PARTITION BY), and (4) compute\nper-feature average by cultivar using a single-scan UNPIVOT + PIVOT instead of\nmultiple UNION ALL subqueries.\n```\n\n### Class Distribution\n\n```sql\n%%sql -r df_class_dist\nSELECT\n    cultivar,\n    cultivar_name,\n    COUNT(*) AS sample_count\nFROM {{df_snow}}\nGROUP BY cultivar, cultivar_name\nORDER BY cultivar\n```\n\nThis query confirms whether the dataset is balanced across the three Wine cultivar classes (0, 1, 2).\n\n### Alcohol Stats per Cultivar\n\n```sql\n%%sql -r df_alcohol_stats\nSELECT\n    cultivar_name,\n    ROUND(MIN(alcohol), 3)  AS min_alcohol,\n    ROUND(AVG(alcohol), 3)  AS avg_alcohol,\n    ROUND(MAX(alcohol), 3)  AS max_alcohol\nFROM {{df_snow}}\nGROUP BY cultivar_name\nORDER BY cultivar_name\n```\n\nThe `%%sql -r \u003Cvariable\u003E` magic captures the result into a Python variable (`df_alcohol_stats`) for downstream use in Python cells.\n\n### Top Samples by Flavanoid Content\n\n```sql\n%%sql -r df_top_flavanoids\nSELECT\n    cultivar_name,\n    ROUND(alcohol, 3)    AS alcohol,\n    ROUND(flavanoids, 3) AS flavanoids,\n    ROUND(proline, 0)    AS proline\nFROM {{df_snow}}\nORDER BY flavanoids DESC\nLIMIT 9\n```\n\n### Average Feature Values per Cultivar\n\n```sql\n%%sql -r df_feature_avgs\nSELECT\n    cultivar_name,\n    ROUND(AVG(alcohol), 3)         AS avg_alcohol,\n    ROUND(AVG(flavanoids), 3)      AS avg_flavanoids,\n    ROUND(AVG(color_intensity), 3) AS avg_color_intensity,\n    ROUND(AVG(proline), 3)         AS avg_proline\nFROM {{df_snow}}\nGROUP BY cultivar_name\nORDER BY cultivar_name\n```\n\nThis reveals how the three cultivars differ on the features most commonly used in Wine classification tasks.\n\n### What Gets Generated\n\nEach SQL cell returns a result table rendered inline in the notebook. For example, the class distribution query returns:\n\n```\n  cultivar  cultivar_name  sample_count\n0        0    Cultivar 0            59\n1        1    Cultivar 1            71\n2        2    Cultivar 2            48\n```\n\nAnd the alcohol stats query returns:\n\n```\n  cultivar_name  min_alcohol  avg_alcohol  max_alcohol\n0   Cultivar 0        11.45       13.745        14.83\n1   Cultivar 1        11.03       12.279        14.10\n2   Cultivar 2        11.03       13.153        14.34\n```\n\n\u003C!-- ------------------------ --\u003E\n## EDA with Python\n\nPython-based EDA focuses on the *shape* of the data — how features are distributed across cultivar classes and how strongly they correlate with each other.\n\n### Prompt\n\nUse this prompt with an AI coding assistant to extend this section:\n\n```\nUsing matplotlib and seaborn, produce three visualisations for the Wine dataset:\n(1) a grid of grouped box plots showing the distribution of every feature broken\nout by cultivar, (2) a lower-triangle 13x13 Pearson correlation heatmap with\nannotated coefficients, and (3) a pairplot of the five most discriminative\nfeatures coloured by cultivar class.\n```\n\n### Grouped Box Plots\n\n```python\nimport matplotlib.pyplot as plt\n\nfeatures = wine.feature_names\nn_cols = 4\nn_rows = (len(features) + n_cols - 1) // n_cols\n\nfig, axes = plt.subplots(n_rows, n_cols, figsize=(18, n_rows * 3.5))\naxes = axes.flatten()\ncolors = ['#29B5E8', '#FF6B35', '#4CAF50']\n\nfor i, feat in enumerate(features):\n    ax = axes[i]\n    data_by_class = [df[df['cultivar'] == c][feat].values for c in [0, 1, 2]]\n    bp = ax.boxplot(data_by_class, patch_artist=True, tick_labels=['C0', 'C1', 'C2'],\n                    medianprops=dict(color='black', linewidth=1.5))\n    for patch, color in zip(bp['boxes'], colors):\n        patch.set_facecolor(color)\n        patch.set_alpha(0.7)\n    ax.set_title(feat, fontsize=9, fontweight='bold')\n    ax.grid(True, alpha=0.3, axis='y')\n\nfor j in range(len(features), len(axes)):\n    axes[j].set_visible(False)\n\nfig.suptitle('Feature Distributions by Cultivar (Grouped Box Plots)', fontsize=14, fontweight='bold')\nplt.tight_layout()\nplt.show()\n```\n\nThe 4x4 grid of box plots shows how each of the 13 chemical features is distributed across the three cultivar classes. Features such as **flavanoids** and **proline** show strong class separation — they are good candidates for classification.\n\n### Correlation Heatmap\n\n```python\nimport numpy as np\nimport seaborn as sns\n\nnumeric_df = df[list(wine.feature_names)]\ncorr = numeric_df.corr()\n\nfig, ax = plt.subplots(figsize=(13, 11))\nsns.heatmap(\n    corr,\n    annot=True, fmt='.2f',\n    cmap='coolwarm', center=0,\n    linewidths=0.4,\n    annot_kws={'size': 7},\n    ax=ax\n)\nax.set_title('Feature Correlation Matrix (13x13)', fontsize=14, fontweight='bold')\nplt.tight_layout()\nplt.show()\n```\n\nThe 13x13 heatmap annotates every Pearson correlation coefficient. Notable strong correlations include **flavanoids** and **total_phenols** (r ≈ 0.86) — meaning these features carry similar information and one could be dropped to reduce multicollinearity before modeling.\n\n### Descriptive Statistics\n\n```python\nstats = df[list(wine.feature_names)].describe().T.round(3)\nprint(stats.to_string())\n```\n\nThis prints count, mean, std, min, 25th/50th/75th percentile, and max for all 13 features in a single transposed table — useful for spotting scale differences before applying `StandardScaler`.\n\n### Pairplot — Key Features by Cultivar\n\n```python\nkey_features = ['alcohol', 'flavanoids', 'color_intensity', 'proline',\n                'od280/od315_of_diluted_wines']\npair_df = df[key_features + ['cultivar_name']].copy()\n\npalette = {'Cultivar 0': '#29B5E8', 'Cultivar 1': '#FF6B35', 'Cultivar 2': '#4CAF50'}\ng = sns.pairplot(pair_df, hue='cultivar_name', palette=palette,\n                 plot_kws={'alpha': 0.6, 's': 25}, diag_kind='kde')\ng.figure.suptitle('Pairplot — Key Features by Cultivar', y=1.02, fontsize=13, fontweight='bold')\nplt.tight_layout()\nplt.show()\n```\n\nThe pairplot of the 5 most discriminative features shows near-linear separability between cultivar classes in 2D projections — a strong signal that a linear or tree-based classifier should achieve high accuracy.\n\n### What Gets Generated\n\nThree figures are rendered inline in the notebook:\n\n**Grouped box plots** — a 4x4 grid showing the distribution of all 13 features split by cultivar class. Features like `flavanoids` and `proline` show clean separation between classes:\n\n![Grouped Box Plots by Cultivar](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/grouped_box_plots.png?v=e378f781)\n\n**Correlation heatmap** — a 13x13 annotated Pearson correlation matrix. Strong positive correlations appear between `flavanoids` and `total_phenols` (r ≈ 0.86):\n\n![Feature Correlation Matrix](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/correlation_heatmap.png?v=e378f781)\n\n**Pairplot** — scatter matrix of the 5 most discriminative features coloured by cultivar, showing near-linear separability:\n\n![Pairplot Key Features by Cultivar](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/pairplot_key_features.png?v=e378f781)\n\n\u003C!-- ------------------------ --\u003E\n## Machine Learning Modeling\n\nThis section preprocesses the data, visualizes the train/test split in PCA space, exposes interactive hyperparameter sliders, trains a Random Forest, and evaluates it with cross-validation.\n\n### Prompt\n\nUse this prompt with an AI coding assistant to extend this section:\n\n```\nSplit the Wine dataset 80/20 with stratification and scale features using\nStandardScaler. Fit a PCA with 2 components and plot the scores coloured by\n(a) train/test split and (b) cultivar class in side-by-side scatter plots. Add\nipywidgets IntSlider widgets for n_estimators (range 10-500, step 10) and\nmax_depth (range 1-20), then train a RandomForestClassifier reading those slider\nvalues, report test-set accuracy, and run 5-fold cross-validation on the full\ndataset.\n```\n\n### Preprocessing: Train/Test Split and Scaling\n\n```python\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\n\nX = df[list(wine.feature_names)].values\ny = df['cultivar'].values\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.2, random_state=42, stratify=y\n)\n\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\n\nprint(f\"Train set: {X_train_scaled.shape[0]} samples\")\nprint(f\"Test set:  {X_test_scaled.shape[0]} samples\")\n```\n\nAn 80/20 stratified split is used so that the class proportions are preserved in both train and test sets. `StandardScaler` is **fit only on the training set** to avoid data leakage — it is then applied to the test set using the training-set statistics.\n\n### PCA Scores Panel Plot\n\n```python\nfrom sklearn.decomposition import PCA\n\nX_full = df[list(wine.feature_names)].values\nX_full_scaled = scaler.transform(X_full)\n\npca = PCA(n_components=2, random_state=42)\nX_pca = pca.fit_transform(X_full_scaled)\nvar_explained = pca.explained_variance_ratio_ * 100\n```\n\nThe PCA scores plot has two panels:\n- **Left**: train samples (blue) and test samples (orange) overlaid in 2D PCA space — confirming the split is representative and not accidentally grouped in one region.\n- **Right**: the same points coloured by cultivar class — confirming that the three classes are largely linearly separable in the first two principal components.\n\n### Interactive Hyperparameter Sliders\n\n```python\nimport ipywidgets as widgets\nfrom IPython.display import display\n\nn_estimators_slider = widgets.IntSlider(\n    value=100, min=10, max=500, step=10,\n    description='n_estimators:',\n    style={'description_width': 'initial'},\n    continuous_update=False\n)\n\nmax_depth_slider = widgets.IntSlider(\n    value=5, min=1, max=20, step=1,\n    description='max_depth:',\n    style={'description_width': 'initial'},\n    continuous_update=False\n)\n\nprint('Adjust sliders then run the next cell to train the model.')\ndisplay(n_estimators_slider, max_depth_slider)\n```\n\nAdjust the sliders, then run the next cell. The model will be retrained with the new values each time you run it.\n\n### Train Random Forest and Cross-Validate\n\n```python\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import cross_val_score\n\nn_estimators = n_estimators_slider.value\nmax_depth = max_depth_slider.value\n\nrf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42)\nrf.fit(X_train_scaled, y_train)\n\ntest_accuracy = rf.score(X_test_scaled, y_test)\nprint(f\"Test set accuracy: {test_accuracy:.4f} ({test_accuracy*100:.1f}%)\")\n\ncv_scores = cross_val_score(rf, scaler.transform(X), y, cv=5, scoring='accuracy')\nprint(f\"\\n5-Fold Cross-Validation:\")\nprint(f\"  Scores: {[f'{s:.3f}' for s in cv_scores]}\")\nprint(f\"  Mean:   {cv_scores.mean():.4f} +/- {cv_scores.std():.4f}\")\n```\n\n### Classification Report\n\n```python\nfrom sklearn.metrics import classification_report\n\ny_pred = rf.predict(X_test_scaled)\nprint(classification_report(y_test, y_pred, target_names=wine.target_names))\n```\n\nThe per-class precision, recall, and F1-score confirm which cultivar classes (if any) are harder for the model to distinguish.\n\n### What Gets Generated\n\nThe PCA scores panel confirms the split is representative and that cultivars are linearly separable in 2D PCA space:\n\n![PCA Scores Panel Plot](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/pca_scores_panel.png?v=e378f781)\n\nThe Random Forest training cell prints accuracy and cross-validation scores:\n\n```\nTraining RandomForest with n_estimators=100, max_depth=5\nTest set accuracy: 0.9722 (97.2%)\n\n5-Fold Cross-Validation:\n  Scores: ['0.944', '0.944', '1.000', '1.000', '0.971']\n  Mean:   0.9722 +/- 0.0249\n```\n\n\u003C!-- ------------------------ --\u003E\n## Post-ML Analysis\n\nPost-training diagnostics help you understand where the model makes mistakes, which features drive its predictions, how well it separates classes across all decision thresholds, and whether additional training data would improve performance.\n\n### Prompt\n\nUse this prompt with an AI coding assistant to extend this section:\n\n```\nAfter training a Random Forest on the Wine dataset, produce four evaluation\nplots: (1) a seaborn heatmap confusion matrix for the test set, (2) a horizontal\nbar chart of feature importances sorted ascending, (3) one-vs-rest ROC curves\nwith AUC scores for all three cultivar classes on a single axes, and (4) a\nlearning curve showing mean training and cross-validation accuracy with +/-1 std\nshading as training set size increases. The learning curve title should reflect\nthe current n_estimators and max_depth values from the ipywidgets sliders.\n```\n\n### Confusion Matrix\n\n```python\nfrom sklearn.metrics import confusion_matrix\n\ncm = confusion_matrix(y_test, y_pred)\n\nfig, ax = plt.subplots(figsize=(6, 5))\nsns.heatmap(\n    cm, annot=True, fmt='d', cmap='Blues',\n    xticklabels=wine.target_names,\n    yticklabels=wine.target_names,\n    ax=ax\n)\nax.set_title('Confusion Matrix', fontsize=13, fontweight='bold')\nax.set_xlabel('Predicted Label')\nax.set_ylabel('True Label')\nplt.tight_layout()\nplt.show()\n```\n\nEach cell shows the count of test samples with a given true label (row) and predicted label (column). Off-diagonal cells represent misclassifications.\n\n### Feature Importances\n\n```python\nimportances = rf.feature_importances_\nsorted_idx = np.argsort(importances)\n\nfig, ax = plt.subplots(figsize=(8, 7))\nax.barh(range(len(sorted_idx)), importances[sorted_idx], color='#29B5E8', alpha=0.8)\nax.set_yticks(range(len(sorted_idx)))\nax.set_yticklabels([wine.feature_names[i] for i in sorted_idx])\nax.set_title('Random Forest Feature Importances', fontsize=13, fontweight='bold')\nplt.tight_layout()\nplt.show()\n```\n\nFeature importances are measured by **mean decrease in impurity** across all trees. Typically, **proline**, **color_intensity**, and **flavanoids** rank highest for the Wine dataset — consistent with the EDA observations.\n\n### ROC Curves (One-vs-Rest)\n\n```python\nfrom sklearn.preprocessing import label_binarize\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.model_selection import cross_val_predict\n\ny_bin = label_binarize(y, classes=[0, 1, 2])\nX_scaled_all = scaler.transform(X)\n_, X_test_all, _, y_test_bin = train_test_split(\n    X_scaled_all, y_bin, test_size=0.2, random_state=42, stratify=y\n)\ny_score = rf.predict_proba(X_test_scaled)\n\ny_cv_score = cross_val_predict(\n    RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42),\n    X_scaled_all, y, cv=5, method='predict_proba'\n)\n```\n\nTwo ROC panels are plotted side by side:\n- **Left** — AUC on the held-out test set (20% of data).\n- **Right** — AUC from 5-fold CV out-of-fold predictions (all 178 samples used for evaluation without data leakage).\n\nComparing the two panels lets you check whether strong test-set AUC is reproducible across multiple splits or is a lucky artifact of the particular 80/20 split.\n\n### Learning Curve\n\n```python\nfrom sklearn.model_selection import learning_curve\n\ntrain_sizes, train_scores, val_scores = learning_curve(\n    RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42),\n    scaler.transform(X), y,\n    cv=5, scoring='accuracy',\n    train_sizes=np.linspace(0.1, 1.0, 10),\n    n_jobs=-1\n)\n\ntrain_mean = train_scores.mean(axis=1)\nval_mean   = val_scores.mean(axis=1)\n```\n\nThe learning curve plots training accuracy and CV accuracy as a function of training set size. A small gap between the two curves at the rightmost point indicates the model is not overfitting and is unlikely to benefit significantly from collecting more data.\n\n### What Gets Generated\n\nFour diagnostic plots are rendered inline:\n\n**Confusion matrix** — true vs predicted labels on the test set. Diagonal cells are correctly classified samples; off-diagonal cells are misclassifications:\n\n![Confusion Matrix](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/confusion_matrix.png?v=e378f781)\n\n**Feature importances** — horizontal bar chart ranked by mean decrease in impurity. `proline`, `color_intensity`, and `flavanoids` are the top predictors:\n\n![Random Forest Feature Importances](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/feature_importances.png?v=e378f781)\n\n**ROC curves** — one-vs-rest AUC side by side for the test set and 5-fold CV out-of-fold predictions. All three cultivars achieve AUC \u003E 0.99:\n\n![ROC Curves One-vs-Rest](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/roc_curves.png?v=e378f781)\n\n**Learning curve** — training and CV accuracy vs dataset size with ±1 std shading. The narrow gap at the right indicates no significant overfitting:\n\n![Learning Curve](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-notebooks-in-workspaces-eda-ml-pipeline/learning_curve.png?v=e378f781)\n\n\u003C!-- ------------------------ --\u003E\n## Summary and Next Steps\n\n### What You Built\n\nIn this guide you built a complete end-to-end classification pipeline inside a single Snowflake Notebook:\n\n- **Setup** — loaded the Wine dataset into a pandas DataFrame, connected to Snowflake via `get_active_session()`, and sanitised column names for SQL compatibility.\n- **SQL EDA** — queried the in-memory pandas DataFrame directly from SQL cells using `{{df_snow}}` Jinja templating to verify class balance, compare alcohol statistics, surface top flavanoid samples, and compare per-cultivar feature averages. SQL cell results are returned as Snowpark pandas (snowpandas) DataFrames (call `.to_pandas()` for downstream pandas operations).\n- **Python EDA** — grouped box plots revealed per-feature class separability; the 13x13 correlation heatmap identified collinear features; a pairplot of the five most discriminative features confirmed near-linear class separability.\n- **PCA scores plot** — confirmed the stratified 80/20 train/test split is representative and that cultivars are largely separable in 2D PCA space.\n- **Random Forest** — trained with interactive `ipywidgets` sliders for `n_estimators` and `max_depth`; validated with 5-fold cross-validation to confirm the result generalizes beyond the single split.\n- **Post-ML analysis** — confusion matrix identified which cultivars are misclassified; feature importances ranked proline, color_intensity, and flavanoids as the top predictors; ROC curves confirmed strong one-vs-rest AUC; the learning curve showed no significant overfitting.\n\n### Next Steps\n\n- **Try other classifiers** — swap in `SVC`, `GradientBoostingClassifier`, or `LogisticRegression` in place of `RandomForestClassifier` to compare performance.\n- **Hyperparameter search** — replace the manual sliders with `GridSearchCV` or `RandomizedSearchCV` from scikit-learn.\n- **Register the model** — use the [Snowflake Model Registry](https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview) to version, log metrics for, and deploy the trained model.\n- **Schedule the notebook** — use [Notebook Scheduling](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-schedule) to retrain the model on a cadence as new data arrives.\n- **Use a real dataset** — replace the Wine dataset with your own table in Snowflake by reading it with `session.table()` or `session.sql()` instead of `load_wine()`.\n\n### Resources\n\n- [Snowflake Notebooks in Workspaces](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-in-workspaces/notebooks-in-workspaces-overview)\n- [Snowflake Model Registry](https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview)\n- [Snowpark Python API](https://docs.snowflake.com/en/developer-guide/snowpark/python/index)\n- [scikit-learn Wine dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#wine-recognition-dataset)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-0b5605ce82","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-2b29a27a7f",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-02663e17c3","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-25",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-bc940ddc88","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-2e4e01c421",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-d4b8f3a527",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-74f159fc8a","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-841a164102","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003EImport 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