Common use cases¶
Review Jupyter notebooks that outline common use cases and machine learning workflows using version 3.x of DataRobot's Python client.
|Use cases for version 2.x||Notebooks for uses cases that use methods for 2.x versions of DataRobot's Python client.|
|Identify money laundering with anomaly detection||How to use a historical financial transaction dataset and train models that detect instances of money laundering.|
|Measure price elasticity of demand||A use case to identify relationships between price and demand, maximize revenue by properly pricing products, and monitor price elasticities for changes in price and demand.|
|Insurance claim triage||How to evaluate the severity of an insurance claim in order to triage it effectively.|
|Predict loan defaults||A use case that reduces defaults and minimizes risk by predicting the likelihood that a borrower will not repay their loan.|
|No-show appointment forecasting||How to build a model that identifies patients most likely to miss appointments, with correlating reasons.|
|Predict late shipments||A use case that determines whether a shipment will be late or if there will be a shortage of parts.|
|Reduce 30-Day readmissions rate||How to reduce the 30-day readmission rate at a hospital.|
|Predict steel plate defects||A use case that helps manufacturers significantly improve the efficiency and effectiveness of identifying defects of all kinds, including those for steel sheets.|
|Predict customer churn||How to predict customers that are at risk to churn and when to intervene to prevent it.|
|Large scale demand forecasting||An end-to-end demand forecasting use case that uses DataRobot's Python package.|
|Predictions for fantasy baseball||An estimate of a baseball player's true talent level and their likely performance for the coming season.|
|Lead scoring||A binary classification problem of whether a prospect will become a customer.|
|Forecast sales with multiseries modeling||How to forecast future sales for multiple stores using multiseries modeling.|
|Identify money laundering with anomaly detection||How to train anomaly detection models to detect outliers.|
|Predict CO₂ levels with out-of-time validation modeling||How to use out-of-time validation (OTV) modeling with DataRobot's Python client to predict monthly CO₂ levels for one of Hawaii's active volcanoes, Mauna Loa.|
|Predict equipment failure||A use case that that determines whether or not equipment part failure will occur.|
|Predict fraudulent medical claims||The identification of fraudulent medical claims using the DataRobot Python package.|
|Generate SHAP-based Prediction Explanations||How to use DataRobot's SHAP Prediction Explanations to determine what qualities of a home drive sale value.|
Updated February 3, 2023
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