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Kumo Engine Examples

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Browse the notebooks and Python examples in the Kumo Engine examples repository. Choose an example below and follow its setup and data prerequisites.

Examples are grouped by industry, with deployment and scope listed for each workflow. Entries marked Commented preview contain demonstrations that are not runnable with the supported package API.

Cross-industry

Notebooks

ExampleDeploymentScope
Build a custom training tableLocalFocused workflow
Choose a static or temporal queryLocalFocused workflow
Choose the customers to scoreDatabricksFocused workflow
Preview graph visualizationLocalCommented preview

SDK code examples

Energy

SDK code examples

Financial services

Notebooks

ExampleDeploymentScope
Predict reported transaction fraud on DatabricksDatabricksModel workflow
Screen accounts for money mulingDatabricksModel workflow
Screen synthetic transactions for reviewLocalSynthetic binary classification
Detect money mule activity in synthetic accountsLocalSynthetic account classification with a baseline and new-account scoring

SDK code examples

Healthcare

Notebooks

ExampleDeploymentScope
Predict appointment attendance in the next 30 daysLocalSynthetic temporal binary classification

Hospitality

SDK code examples

Media and entertainment

Notebooks

ExampleDeploymentScope
Predict low ratings for movies on SnowflakeSnowflakeModel workflow
Predict the content formats each subscriber will useLocalSynthetic multilabel classification

SDK code examples

Retail

Notebooks

SDK code examples

ExampleDeploymentScope
Combine two-part spend predictions with explicit row alignmentLocalFocused workflow
Fit H&M lifetime value or recommendation models with supplied example weightsLocalModel workflow
Forecast 28 daily sales values from the public M5 competitionLocalModel workflow
Forecast three 30-day order counts for each customerLocalModel workflow
Match Walmart and Amazon products using the public labeled entity-matching pairsLocalModel workflow
Predict 28-day customer churn by sales channel in SnowflakeSnowflakeModel workflow
Predict H&M churn, conditional lifetime value, recommendations or daily revenueLocalModel workflow
Predict H&M repeat purchase over 90 days and report complementary churn riskLocalModel workflow
Predict conditional 30-day maximum purchase quantity using the public CustomerLTV mini datasetLocalModel workflow
Predict each active customer’s order count for the next 90 daysLocalModel workflow
Predict the size of the next Instacart basket within 30 daysLocalModel workflow
Predict whether an active customer orders again in the next 90 daysLocalModel workflow
Predict whether each active customer orders online, offline, or bothLocalModel workflow
Preprocess customer-location entities with read-only Snowflake SQLSnowflakeFocused workflow
Rank product relationships without a future prediction windowLocalModel workflow
Rank products each customer may purchase in the next 90 daysLocalModel workflow
Recommend coupons from the public Coupon Purchase Prediction competitionLocalModel workflow
Recommend future customers for each product in the public Olist datasetLocalModel workflow
Recommend stores for each user/location combination after explicit preprocessingLocalModel workflow
Recommend twelve related H&M articles from a prepared article-pair tableLocalModel workflow
Reload a trusted AdventureWorks model and score a compatible snapshotLocalModel workflow
Run paired H&M model scripts as local processes and retry only failed fitsLocalFocused workflow
Train an AdventureWorks repeat-order model and score active customersLocalModel workflow
Train one half of a two-part H&M expected-spend modelLocalModel workflow

Science

SDK code examples

ExampleDeploymentScope
Forecast monthly sunspot counts from the public SILSO seriesLocalModel workflow