Welcome to the Diabetes Prediction machine learning project repository! This project focuses on predicting the likelihood of diabetes based on various health parameters using machine learning techniques.
- Introduction
- Why This Project
- Dataset
- Features
- Models Implemented
- Evaluation Metrics
- Setup and Installation
- Demo
- Contributing
- Challenges Faced
- Lessons Learned
- License
- Contact
This repository contains a machine learning project focused on predicting diabetes onset using supervised learning techniques. It includes data preprocessing, model development, evaluation, and deployment aspects of the project.
The primary motivation behind creating this project is to leverage machine learning to address a significant health issue. Diabetes affects millions worldwide, and early prediction can significantly improve management and outcomes for individuals at risk.
The dataset used for this project contains information about several health indicators such as glucose levels, blood pressure, BMI, etc., collected from patients. It is crucial for predicting the likelihood of diabetes onset.
- Data Preprocessing: Cleaned and transformed dataset for machine learning model compatibility.
- Model Development: Trained multiple machine learning models to predict diabetes onset.
- Model Evaluation: Evaluated models using appropriate metrics to ensure accuracy and reliability.
- Deployment: Implemented a simple Streamlit web application for demonstrating model predictions (if applicable).
Several machine learning models were implemented and evaluated:
- Logistic Regression
- Decision Tree Classifier
- Random Forest Classifier
- Support Vector Machine (SVM)
- Neural Network (if applicable)
Each model's performance was compared based on metrics such as accuracy, precision, recall, and F1-score.
The models were evaluated using the following metrics:
- Accuracy: Overall correctness of the predictions.
- Precision: Proportion of true positives among all positive predictions.
- Recall: Proportion of true positives identified correctly.
- F1-score: Harmonic mean of precision and recall, providing a balance between the two metrics.
To run this project locally, follow these steps:
-
Clone the repository:
git clone https://github.com/Md-Emon-Hasan/ML-Project-Diabetes-Prediction.git
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Navigate to the project directory:
cd ML-Project-Diabetes-Prediction -
Install the required dependencies:
pip install -r requirements.txt
-
Run the notebooks or scripts as per your requirements.
Explore the live demo of the project here.
Contributions to enhance or expand the project are welcome! Here's how you can contribute:
-
Fork the repository.
-
Create a new branch:
git checkout -b feature/new-feature
-
Make your changes:
- Implement new features, improve model performance, or enhance documentation.
-
Commit your changes:
git commit -am 'Add a new feature or update' -
Push to the branch:
git push origin feature/new-feature
-
Submit a pull request.
During the development of this project, the following challenges were encountered:
- Handling missing data and outliers in the dataset.
- Selecting the most appropriate machine learning algorithms for prediction.
- Ensuring model robustness and generalization.
Key lessons learned from this project include:
- Practical application of machine learning algorithms.
- Evaluation and selection of appropriate metrics based on project goals.
- Implementation and deployment of machine learning models for practical applications.
This project is licensed under the Apache License 2.0. See the LICENSE file for more details.
- Email: [email protected]
- WhatsApp: +8801834363533
- GitHub: Md-Emon-Hasan
- LinkedIn: Md Emon Hasan
- Facebook: Md Emon Hasan
Feel free to reach out for any questions or feedback regarding the project!
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