https://github.com/bhaveshbhakta/diabetes-prediction
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https://github.com/bhaveshbhakta/diabetes-prediction
diabetes-prediction flask machine-learning python scikit-learn svm web-development
Last synced: 6 months ago
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- Host: GitHub
- URL: https://github.com/bhaveshbhakta/diabetes-prediction
- Owner: BhaveshBhakta
- Created: 2024-11-17T05:47:40.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-01T14:49:09.000Z (almost 2 years ago)
- Last Synced: 2025-02-01T01:31:03.105Z (over 1 year ago)
- Topics: diabetes-prediction, flask, machine-learning, python, scikit-learn, svm, web-development
- Language: Jupyter Notebook
- Homepage: https://diabetes-prediction-q603.onrender.com
- Size: 7.49 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Diabetes Prediction
This repository showcases a machine learning-driven solution for predicting diabetes, combining multiple advanced algorithms with an interactive and dynamic web-based interface.
## Project Overview
Diabetes is a chronic disease affecting millions of people worldwide, and early detection plays a vital role in its management and prevention. This project aims to assist in this process by using a predictive model powered by machine learning, coupled with a user-friendly web interface that makes the tool accessible to everyone.
## Key Features
### Machine Learning Models
- **Algorithm Diversity**: The model leverages multiple algorithms, including Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors Classifier, and Support Vector Machines (SVM).
- **Ensemble Learning**: A stacking classifier is employed to integrate predictions from these models, providing the most accurate and reliable output possible.
### User Interaction
- **Input Parameters**: The interface requires users to input medical parameters such as Glucose Level, Blood Pressure, BMI, Insulin Level, etc., which are critical in diagnosing diabetes.
- **Prediction Output**: Based on the input values, the tool predicts whether diabetes is present or not, helping users understand their health risks.
### Web Design and User Experience
- **Lenis for Mouse Trails**: Adds a visually appealing dynamic mouse trail effect for an engaging user interaction.
- **GSAP Animations**: Implements smooth animations that elevate the overall look and feel of the website.
- **ScrollTrigger for Dynamic Routing**: Enables seamless transitions between pages and sections, enhancing navigation and responsiveness.
## Technical Highlights
This project integrates cutting-edge machine learning techniques with modern web development practices:
- **Data Analysis**: The algorithms are trained on comprehensive datasets, ensuring robustness and accuracy.
- **Scalability**: Designed with extensibility in mind, allowing for future incorporation of additional features or models.
- **Responsive Design**: The interface adapts to various devices, ensuring accessibility for all users.
## Purpose and Applications
The primary goal of this project is to make an impactful contribution to healthcare by providing an efficient and interactive tool for diabetes prediction. While targeted at healthcare professionals and patients, the platform also serves as an excellent educational resource for individuals learning about machine learning applications in medicine.
## Installation
Follow these steps to set up and run the project on your local system:
1. Clone the repository to your local machine:
```bash
git clone https://github.com/BhaveshBhakta/diabetes-prediction.git
```
2. Navigate to the project directory:
```bash
cd diabetes-prediction
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Run the application:
```bash
python app.py
```
5. Open your browser and go to `http://127.0.0.1:5000` to interact with the web application.
## Contributing
Contributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request.
## Website Overview
