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https://github.com/bharath-tars/streamlit_diabsynth
Predictive Health Analytics for Diabetic Risk Assessment and Personalized Reporting WebApp using Streamlit
https://github.com/bharath-tars/streamlit_diabsynth
deeplearning mahine-learning numpy onrender-deploy pandas seaborn streamlit
Last synced: 28 days ago
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Predictive Health Analytics for Diabetic Risk Assessment and Personalized Reporting WebApp using Streamlit
- Host: GitHub
- URL: https://github.com/bharath-tars/streamlit_diabsynth
- Owner: Bharath-tars
- License: gpl-3.0
- Created: 2024-01-19T18:10:57.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-06T18:34:09.000Z (7 months ago)
- Last Synced: 2024-12-07T08:19:30.966Z (3 months ago)
- Topics: deeplearning, mahine-learning, numpy, onrender-deploy, pandas, seaborn, streamlit
- Language: Python
- Homepage: https://diabsynth.onrender.com/
- Size: 55.7 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Predictive Health Analytics for Diabetic Risk Assessment and Personalized Reporting WebApp using Streamlit
## Table of Contents
- [Overview](#overview)
- [Aim](#aim)
- [Mission](#mission)
- [Technologies Used](#technologies-used)
- [Installation](#installation)
- [Team](#team)
## Overview
This project aims to predict the risk of diabetes in individuals based on various features such as pregnancies, insulin level, age, and BMI. The dataset used for this project is sourced from Kaggle, originally provided by the National Institute of Diabetes and Digestive and Kidney Diseases.
## Aim
To develop a predictive health analytics tool for assessing diabetic risk and providing personalized reports.
## Mission
To leverage machine learning for early detection of diabetes, enabling timely medical intervention and improving health outcomes.
## Learning Objective
- Understand the end-to-end process of developing a machine learning model.
- Gain experience in deploying applications on cloud platforms like Heroku.
- Learn to build interactive web applications using Streamlit.
## Technical Aspect
- Training a machine learning model using scikit-learn.
- Building and hosting a Strealit web app on Heroku.
- User input for features such as pregnancies, insulin level, age, BMI, etc., followed by a prediction display.
## Technologies Used
- Python
- scikit-learn
- strealit
- seaborn
- Heroku
## Installation
1. Clone this repository and unzip it.
2. Navigate into the project directory.
```bash
cd filename
```
3. Create a virtual environment with Python 3 and activate it.
```bash
python3 -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
4. Install the required packages.
```bash
pip install -r requirements.txt
```
5. Run
Execute the following command to start the application:
```bash
python app.py
```
## Contributors
- [Bharath](https://github.com/Bharath-tars)
- Pooja Chinta
- Yenuganti Sai Kumar
## Credits
This repository was created with ❤️ by Sudarsanam Bharath.