{"id":29016536,"url":"https://github.com/memgraph/insurance-fraud","last_synced_at":"2025-06-25T22:30:36.468Z","repository":{"id":58999094,"uuid":"531508036","full_name":"memgraph/insurance-fraud","owner":"memgraph","description":"Insurance companies lose a lot of money on different kinds of fraud. 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Using Memgraph, utilizes the power of graphs to model the data and detect fraudulent claims. Extracts features using graph algorithms and uses machine learning to evaluate insurance claims.\n\n## Data model\n\u003cp align=\"left\"\u003e\n  \u003cimg width=\"1000px\" src=\"img/lab_graph_schema.png\" alt=\"memgraph-tutorial-credit-card-fraud-data-model\"\u003e\n\u003c/p\u003e\n\n\n## Running the demo\n\nYou can set up the project with poetry:\n```\npoetry install\n```\n\nOr, if you don't want to use poetry, simply:\n```\npip install -r requirements.txt\n```\n\nThe demo is located in `./fraud_detection_demo.ipynb`. \n\nRecommended Python version is 3.9, since `sklearn` package doesn't yet support Python 3.10.\n\n## Using the dataset only\n\nIf you want to just use the dataset, try running:\n```\npython dataset/data_generator.py 1000 300\n```\nAnd to import it into Memgraph, first start Memgraph, and then run the load script:\n```\ndocker run -it -p 7687:7687 -p 3000:3000 memgraph/memgraph-platform\n\npython load_demo_dataset.py \n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmemgraph%2Finsurance-fraud","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmemgraph%2Finsurance-fraud","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmemgraph%2Finsurance-fraud/lists"}