https://github.com/praveendecode/sugar-sense-ai
Diabetes Prediction: Leveraging machine learning, clinical data, and demographics to build predictive models. Interactive web app powered by Streamlit. Explore factors affecting health
https://github.com/praveendecode/sugar-sense-ai
Last synced: about 1 month ago
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Diabetes Prediction: Leveraging machine learning, clinical data, and demographics to build predictive models. Interactive web app powered by Streamlit. Explore factors affecting health
- Host: GitHub
- URL: https://github.com/praveendecode/sugar-sense-ai
- Owner: praveendecode
- Created: 2023-09-15T10:22:25.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-15T10:27:01.000Z (over 1 year ago)
- Last Synced: 2025-02-09T13:35:10.290Z (3 months ago)
- Language: Python
- Size: 2.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Sugar-Sense-AI
Diabetes Prediction: Leveraging machine learning, clinical data, and demographics to build predictive models. Interactive web app powered by Streamlit. Explore factors affecting health
## Project Description
Diabetes Prediction is a machine learning project focused on predicting diabetes risk using clinical and demographic data. It also delves into the factors impacting health, offering insights for healthcare professionals. The project harnesses data analytics and predictive modeling to improve patient outcomes and reduce healthcare costs.
## Goals of the Project
- **Predictive Modeling:** Develop machine learning models to predict diabetes risk from clinical and demographic data.
- **Data Exploration:** Analyze the dataset to discover patterns, correlations, and factors affecting health.
- **Interactive Web App:** Create an engaging web application using Streamlit to visualize and interact with the predictions.
- **Decision Support:** Assist healthcare decision-makers with insights for resource allocation and patient care strategies.
## Skills Covered
- **Data Preprocessing:** Cleaning, transforming, and preparing data for analysis.
- **Machine Learning:** Building predictive models for healthcare risk assessment.
- **Data Exploration:** Identifying trends and patterns in healthcare data.
- **Data Visualization:** Creating clear and informative visuals for data presentation.
- **Healthcare Analytics:** Applying data analytics techniques to the healthcare domain.
- **Communication:** Effectively conveying insights and recommendations to stakeholders.
## Connect with Us
For queries and collaboration, connect with us on [LinkedIn](https://www.linkedin.com/in/your-profile).