https://github.com/ahmed-maher77/diabetes-prediction-app-using-machine-learning
Diabetes Prediction: Using machine learning to classify individuals as diabetic or non-diabetic based on health data, enabling early intervention and improved healthcare outcomes.
https://github.com/ahmed-maher77/diabetes-prediction-app-using-machine-learning
ai css data-science gradientboostinclassifier javascript logisticregression machine-learning matplotlib numpy pandas python randomforestclassifier seaborn streamlit supportvectormachine webdevelopment
Last synced: 2 months ago
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Diabetes Prediction: Using machine learning to classify individuals as diabetic or non-diabetic based on health data, enabling early intervention and improved healthcare outcomes.
- Host: GitHub
- URL: https://github.com/ahmed-maher77/diabetes-prediction-app-using-machine-learning
- Owner: Ahmed-Maher77
- Created: 2024-04-12T19:06:31.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-21T00:34:09.000Z (about 2 years ago)
- Last Synced: 2025-01-24T06:45:39.154Z (over 1 year ago)
- Topics: ai, css, data-science, gradientboostinclassifier, javascript, logisticregression, machine-learning, matplotlib, numpy, pandas, python, randomforestclassifier, seaborn, streamlit, supportvectormachine, webdevelopment
- Language: CSS
- Homepage:
- Size: 396 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Diabetes Predictor - using Machine Learning
Diabetes is a chronic health condition affecting millions worldwide. Early detection and management are crucial for preventing complications and improving patient outcomes. This project leverages the power of machine learning to predict the likelihood of diabetes in individuals based on various health parameters such as glucose levels, BMI, age, and more.
Using a dataset containing historical patient information, advanced machine learning algorithms are trained to analyze patterns and identify predictive features associated with diabetes. The resulting model can accurately classify individuals into diabetic or non-diabetic categories, providing valuable insights for healthcare practitioners and empowering individuals to take proactive measures for their health.
By harnessing the capabilities of machine learning, this project aims to enhance diabetes diagnosis, facilitate early intervention, and ultimately contribute to better healthcare outcomes for individuals at risk of this prevalent disease.
🌐**Demo (Live Preview):** https://ai-diabetes-predictor-app.streamlit.app
➲ **Notebook (ML Code):** kaggle.com/code/ahmedmaheralgohary/diabetes-prediction
🎥**Watch Video on LinkedIn:** coming soon
## 💻 Used Technologies
- **Python** → Core language for backend logic and machine learning.
- **Streamlit** → Framework for building interactive ML web applications.
- **JavaScript** → Adds interactivity and client-side enhancements.
- **CSS** → Custom styling for modern, responsive UI.
**📊 Data & Machine Learning**
- **Python Libraries** →
- **pandas, numpy**: Data manipulation and numerical analysis.
- **matplotlib, seaborn**: Data visualization and exploratory analysis.
- **scikit-learn**: ML model training, evaluation, and preprocessing.
- **ML Algorithms** → Implemented and evaluated multiple classifiers:
- Logistic Regression
- Support Vector Machine (SVM)
- Random Forest Classifier
- Gradient Boosting Classifier
## 👀 Website Preview:
### 📥 Installation Instructions for Local Setup:
To download and run this project locally:
```bash
pip install -r requirements.txt
streamlit run app.py
```
## 📁 Project Structure
```
Diabetes Prediction App/
├── app.py # Main Streamlit application
├── Diabetes-Prediction-ML-Model.sav # Trained ML model
├── requirements.txt # Python dependencies
├── style.css # Custom styling
└── README.md # Project documentation
```
## 📬 Contact & Contribution
- 🧑💻 **Portfolio:** https://ahmedmaher-portfolio.vercel.app/
- 🔗 **LinkedIn:** https://www.linkedin.com/in/ahmed-maher-algohary
- 📧 **Email:** ahmedmaher.dev1@gmail.com
> Contributions, suggestions, and bug reports are welcome. Feel free to open issues or pull requests.