Python v2.x use cases¶
Review Jupyter notebooks that outline common use cases and machine learning workflows using DataRobot's Python client.
|Predict loan defaults
|A use case that reduces defaults and minimizes risk by predicting the likelihood that a borrower will not repay their loan.
|Predict late shipments
|A use case that determines whether a shipment will be late or if there will be a shortage of parts.
|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.
|Reduce 30-Day readmissions rate
|A use case to reduce the 30-day readmission rate at a hospital.
|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.
|Predict equipment failure
|A use case that that determines whether or not equipment part failure will occur.
|Identify money laundering with anomaly detection
|How to train anomaly detection models to detect outliers.
|A binary classification problem of whether a prospect will become a customer.
|Predict fraudulent medical claims
|The identification of fraudulent medical claims using the DataRobot Python package.
|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.
|Forecast sales with multiseries modeling
|How to forecast future sales for multiple stores using multiseries modeling.
|Predictions for fantasy baseball
|An estimate of a baseball player's true talent level and their likely performance for the coming season.
|Predict customer churn
|How to predict customers that are at risk to churn and when to intervene to prevent it.
|Configure datetime partitioning
|How to use datetime partitioning to guard a project against time-based target leakage.