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https://github.com/meet57/nourishnet
NourishNet - Welcome to the NourishNet Project! This initiative, part of the Advanced Database Topics course, addresses childhood malnutrition, a critical global issue, using advanced data analysis methods.
https://github.com/meet57/nourishnet
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NourishNet - Welcome to the NourishNet Project! This initiative, part of the Advanced Database Topics course, addresses childhood malnutrition, a critical global issue, using advanced data analysis methods.
- Host: GitHub
- URL: https://github.com/meet57/nourishnet
- Owner: Meet57
- Created: 2024-04-23T16:07:46.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-04-23T16:10:25.000Z (9 months ago)
- Last Synced: 2024-11-10T07:02:02.666Z (2 months ago)
- Language: Jupyter Notebook
- Homepage: https://nourish-net.streamlit.app/
- Size: 42.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# NourishNet (Group 2)
Welcome to the NourishNet Project! This initiative, part of the Advanced Database Topics course, addresses childhood malnutrition, a critical global issue, using advanced data analysis methods.
## Project Overview
Childhood malnutrition poses significant health risks worldwide. The NourishNet Project aims to combat this challenge by employing advanced data analysis techniques and Gemini AI to understand the intricate relationship between diet and malnutrition.
## Live Demo
[NourishNet](https://nourish-net.streamlit.app/)
## Features
- Integration of advanced data analysis techniques such as ARIMA, K-Means clustering, PCA.
- Utilization of Gemini AI for textual insights and recommendations.
- Examination of 51 dietary factors to uncover hidden patterns and identify malnutrition hotspots.
- User-friendly interface empowering stakeholders to devise targeted interventions.
- Visualization of results to highlight global malnutrition trends and dietary patterns.
- Access to various datasets for analysis.
## How to Use
1. Clone the repository to your local machine.
2. Install the necessary dependencies specified in the requirements.txt file.
3. Run the NourishNet.py file to start the application.
4. Explore the user-friendly interface to access analysis results and insights.
5. Customize analysis by selecting different filters and parameters.
## Run Locally
Clone the project
```bash
git clone https://github.com/kishanmodi/Nourish-Ne.git
```
Go to the project directory
```bash
cd Nourish-Net
```
Install dependencies
```bash
pip3 install -r requirements.txt
```
Start the server
```bash
python3 NourishNet.py
```
```bash
streamlit run NourishNet.py
```
## Datasets
1. [Global Dietary Database](https://globaldietarydatabase.org/)
2. [UNICEF JME Data Warehouse](https://data.unicef.org/topic/nutrition/malnutrition/)
## Folder Structure
- `data/`: Contains datasets used for analysis.
- `pages/`: Include Pages of each Section.
- `NourishNet.py`: Main Python script to run the application.
## Contributors
- Kishan Modi
- Meet Patel
- Malhar Raval
- Aditya Tohan
## License
This project is licensed for the Advanced Database Topics course at the University of Windsor during the Winter term of 2024.
## Acknowledgments
We express gratitude to Dr. Shafaq Khan, our instructor, GAs and our peers for their invaluable guidance and support throughout the development of this project.