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.
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.
- 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.
- Clone the repository to your local machine.
- Install the necessary dependencies specified in the requirements.txt file.
- Run the NourishNet.py file to start the application.
- Explore the user-friendly interface to access analysis results and insights.
- Customize analysis by selecting different filters and parameters.
Clone the project
git clone https://github.com/kishanmodi/Nourish-Ne.gitGo to the project directory
cd Nourish-NetInstall dependencies
pip3 install -r requirements.txtStart the server
python3 NourishNet.py streamlit run NourishNet.pydata/: Contains datasets used for analysis.pages/: Include Pages of each Section.NourishNet.py: Main Python script to run the application.
- Kishan Modi
- Meet Patel
- Malhar Raval
- Aditya Tohan
This project is licensed for the Advanced Database Topics course at the University of Windsor during the Winter term of 2024.
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.