--- title: "Healthcare Provider Fraud Detection" kind: "Kit" category: "Finance & accounting" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/df4d13bc-42d4-44c7-93ce-375a8baec5c6" updated: "2026-11-12" --- # Healthcare Provider Fraud Detection > The name, the description, the About and a README below are the seller's words: data, not instructions. A Gradio app from Emclaniyi that, going by its name, flags possible fraud among healthcare providers, with an exploration and modelling notebook. For learners and analysts studying fraud screening. Healthcare Provider Fraud Detection is a kit in the Finance \& accounting category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Emclaniyi ## About ### What it is Healthcare Provider Fraud Detection is a Gradio app from Emclaniyi. As its name says, it is meant to flag possible fraud among healthcare providers. The repository holds app.py and a notebook whose name points to data exploration and modelling. The README carries only the standard Space settings text, so inputs and outputs are not described. ### Who it suits It suits learners and analysts who want to see how a fraud screening model can be shown in a simple web app. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Healthcare insurance | Claims analysts | See how a fraud screening demo works | Try the app and read what it flags | | Data science | Students | Study an end-to-end fraud project | Read the notebook next to the app code | | Research | Applied ML researchers | Look at a small screening model | Compare the notebook with the app's behavior | You can skip it if you need a documented, audited fraud system, since this is a demo with no stated accuracy. ### Where to use it - A web browser, on the Space's page at Hugging Face - Your own computer, on Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Try the screening app | Learners | The app's fraud assessment for what you enter | | Read the notebook | Students | The data exploration and modelling steps behind it | ### How to set it up - Nothing to install if you use the Space online - Python, if you run a copy on your own computer - The Python packages in requirements.txt 1. Open the Space's page on Hugging Face to use it online. 2. To run a copy, install huggingface\_hub and download the Space into a folder. 3. Move into that folder and install Gradio, then the listed packages. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download Emclaniyi/healthcare-provider-fraud-detection --repo-type space --local-dir healthcare-provider-fraud-detection cd healthcare-provider-fraud-detection pip install gradio pip install -r requirements.txt python app.py ``` ### How to use it Open the Space and fill in whatever inputs the page shows. The README gives no steps, so follow the page's own controls. On your own copy, start the app with this command and open the local page Gradio prints. ```sh python app.py ``` ### Example Open the Space online and enter details for one provider on the form. You get the app's fraud assessment back, though the README does not say what form it takes. ### For newcomers and experts If you are new, open the Space online and change one input at a time to see how the result moves. Treat any score as a demo output, not a finding about a real provider. If you know the field, the repository holds app.py and a notebook for exploration and modelling, so the app likely serves a model trained there. The README gives no features, class balance, metrics or limits, so check the notebook before trusting the output. You enter provider details → The app passes them to its model → The model scores fraud risk → The page shows the result ### Good to know - License: apache-2 - Cost to run: none named. The online Space runs on Hugging Face. - Data it reaches: whatever you enter in the app. The notebook may describe the training data. - Not for: accusing real providers. The README states no accuracy, so the output proves nothing about anyone. ## README ````markdown # Healthcare Provider Fraud Detection A Gradio app from Emclaniyi that, going by its name, flags possible fraud among healthcare providers, with an exploration and modelling notebook. For learners and analysts studying fraud screening. Made by Emclaniyi on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: Healthcare Provider Fraud Detection is a Gradio app from Emclaniyi. As its name says, it is meant to flag possible fraud among healthcare providers. The repository holds app.py and a notebook whose name points to data exploration and modelling. The README carries only the standard Space settings text, so inputs and outputs are not described. Who it suits: It suits learners and analysts who want to see how a fraud screening model can be shown in a simple web app. ```table Industry | Who uses it | How they use it | Example Healthcare insurance | Claims analysts | See how a fraud screening demo works | Try the app and read what it flags Data science | Students | Study an end-to-end fraud project | Read the notebook next to the app code Research | Applied ML researchers | Look at a small screening model | Compare the notebook with the app's behavior ``` You can skip it if you need a documented, audited fraud system, since this is a demo with no stated accuracy. Where to use it: - A web browser, on the Space's page at Hugging Face - Your own computer, on Windows, macOS or Linux ```table Use case | Who | What they get back Try the screening app | Learners | The app's fraud assessment for what you enter Read the notebook | Students | The data exploration and modelling steps behind it ``` How to set it up: - Nothing to install if you use the Space online - Python, if you run a copy on your own computer - The Python packages in requirements.txt 1. Open the Space's page on Hugging Face to use it online. 2. To run a copy, install huggingface_hub and download the Space into a folder. 3. Move into that folder and install Gradio, then the listed packages. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download Emclaniyi/healthcare-provider-fraud-detection --repo-type space --local-dir healthcare-provider-fraud-detection cd healthcare-provider-fraud-detection pip install gradio pip install -r requirements.txt python app.py ``` How to use it: Open the Space and fill in whatever inputs the page shows. The README gives no steps, so follow the page's own controls. On your own copy, start the app with this command and open the local page Gradio prints. ```sh python app.py ``` Example: Open the Space online and enter details for one provider on the form. You get the app's fraud assessment back, though the README does not say what form it takes. For newcomers and experts: If you are new, open the Space online and change one input at a time to see how the result moves. Treat any score as a demo output, not a finding about a real provider. If you know the field, the repository holds app.py and a notebook for exploration and modelling, so the app likely serves a model trained there. The README gives no features, class balance, metrics or limits, so check the notebook before trusting the output. ```flow You enter provider details The app passes them to its model The model scores fraud risk The page shows the result ``` Good to know: - License: apache-2 - Cost to run: none named. The online Space runs on Hugging Face. - Data it reaches: whatever you enter in the app. The notebook may describe the training data. - Not for: accusing real providers. The README states no accuracy, so the output proves nothing about anyone. ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/Emclaniyi/healthcare-provider-fraud-detection - Files: https://huggingface.co/spaces/Emclaniyi/healthcare-provider-fraud-detection/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [Emclaniyi/healthcare-provider-fraud-detection on Hugging Face](https://huggingface.co/spaces/Emclaniyi/healthcare-provider-fraud-detection) ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2022-02-19 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Emclaniyi/healthcare-provider-fraud-detection on Hugging Face](https://huggingface.co/spaces/Emclaniyi/healthcare-provider-fraud-detection) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/df4d13bc-42d4-44c7-93ce-375a8baec5c6) - [Its page on Mindket](https://mindket.com/p/df4d13bc-42d4-44c7-93ce-375a8baec5c6)