Abstract
The insurance industry heavily relies on efficient data processing and risk detection to maintain operational effectiveness and customer satisfaction. This case study defines the use of Databricks, a unified analytics platform, combined with PySpark, for processing claims data and detecting potential risks. We detail the setup of the Databricks environment, the import and cleaning of claims data, feature engineering, building a risk detection model, and evaluating its performance. Visual aids such as diagrams and flowcharts are included to illustrate the process. Key Words: Claims Data, Risk Detection, Databricks, PySpark, Data Processing, Machine Learning
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