{"id":24583567,"url":"https://github.com/sai-manas/diabetes_predictor_ml","last_synced_at":"2026-05-11T16:37:12.371Z","repository":{"id":207527643,"uuid":"719410748","full_name":"sai-manas/Diabetes_Predictor_ML","owner":"sai-manas","description":"Diabetes Predictor Web App  Predict diabetes in patients using classification models such as Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machines. 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The implementation includes Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machines. The selection process involved a comprehensive comparison, evaluation, and cross-validation to identify the most effective model. After rigorous cross-validation and metric analysis, the Logistic Regression model emerged as the optimal choice, exhibiting superior performance with an accuracy of 80%.\n\nThe Logistic Regression model, deemed the most reliable, has been deployed in a flask web application using AWS. The web application utilizes the `LogisticModel.pkl` file, which contains the trained Logistic Regression model, offering a user-friendly interface for diabetes prediction.\n\n## Screen Recording of Diabetes predictor application deployed on AWS\nhttps://github.com/sai-manas/Diabetes_Predictor_ML/assets/106865226/030066b0-c8db-46ea-add6-499387c57783\n\n## Jupyter Notebook\n\n1. [**Logistic Regression:**](https://github.com/sai-manas/Diabetes_Predictor_ML/blob/main/Notebooks/Logistic_Regresssion.ipynb) Notebook focusing on Logistic Regression classification.\n2. [**Decision Tree:**](https://github.com/sai-manas/Diabetes_Predictor_ML/blob/main/Notebooks/Decision_Tree_Classifier.ipynb) Notebook dedicated to Decision Tree classification.\n3. [**Naive Bayes:**](https://github.com/sai-manas/Diabetes_Predictor_ML/blob/main/Notebooks/Naive_Bayes_GaussianNB.ipynb) Notebook demonstrating the implementation of Naive Bayes classification.\n4. [**Support Vector Machines:**](https://github.com/sai-manas/Diabetes_Predictor_ML/blob/main/Notebooks/Support_Vector_Classification.ipynb) Notebook showcasing Support Vector Machines classification.\n\n## Technologies Used\n\n- **Programming Language:** Python\n- **Libraries:** Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn , Pickle, Warnings\n- **Web Framework:** Flask\n- **Frontend:** HTML, CSS\n- **Deployment:** AWS Elastic Beanstalk, AWS CodePipeline\n\n## Model Evaluation\n\nEach notebook evaluates the performance of the respective model using various metrics, including accuracy, classification report, cross-validation score, and Grid Search CV best score. The Logistic Regression model exhibited promising results, providing valuable insights into Diabetes prediction.\n\n## Model Deployment\n\nTo make the model accessible, I developed a web application using Flask, a Python web framework. The Logistic Regression model has been deployed in a web application using the pickle library. \n\nThe application is deployed on AWS Elastic Beanstalk, with continuous integration and deployment facilitated by AWS CodePipeline.\n\n## How to use\n\n### I. Notebook Usage\n   - Explore the notebooks to understand the implementation and evaluation of different Classification models [Note Books](https://github.com/sai-manas/Diabetes_Predictor_ML/tree/main/Notebooks).\n\n\u003e Hosted this web application using Flask and deployed on AWS\n### II. Flask\n1. **Clone the GitHub Repository:**\n   - Open your terminal or command prompt.\n   - Navigate to the directory where you want to clone the repository.\n   - Run the following command to clone my GitHub repository:\n     ```\n     git clone https://github.com/my-username/my-repo.git\n     ```\n   - Replace `my-username` with my GitHub username and `my-repo` with the name of my repository.\n     \n2. **Install Python:**\n   - If you haven't already, make sure you have Python installed on your system. You can download it from the official Python website (https://www.python.org/downloads/) and follow the installation instructions for your operating system.\n     \n3. **Create a Virtual Environment (Optional but recommended):**\n   - It's a good practice to create a virtual environment to isolate the dependencies of your project. You can create a virtual environment using the following command:\n     ```\n     python -m venv venv\n     ```\n4. **Activate the Virtual Environment (Optional but recommended):**\n   - Activate the virtual environment using the appropriate command based on your operating system:\n     - On Windows:\n       ```\n       venv\\Scripts\\activate\n       ```\n     - On macOS and Linux:\n       ```\n       source venv/bin/activate\n       ```\n5. **Install Required Packages:**\n   - Navigate to the root folder of your cloned repository.\n   - Run the following command to install the required Python packages listed in the `requirements.txt` file:\n     ```\n     pip install -r requirements.txt\n     ```\n6. **Run the Flask Application:**\n   - Once the packages are installed, you can start your Flask application. In your repository's root folder, you should typically have a file named `application.py`, which is the main Flask application file.\n   - Run the application with the following command:\n     ```\n     python application.py\n     ```\n7. **Access the Application:**\n   - Your Flask application should now be running. You can access it in your web browser by navigating to `http://localhost:5000` or the URL provided by your application.\n\n### III. AWS (ELastic Beanstalk,Code Pipeline)\n1. **Fork the GitHub Repository:**\n   - Click the \"Fork\" button in the top-right corner of this repository's page.\n   - This action will create a copy of my repository under your GitHub account.\n   - Use this as a source for deploying application in AWS\n     \n2. **Deploy application using ELastic Beanstalk and Code Pipeline in AWS**\n   - Follow the steps in this Article for deploying Web application in AWS. URL - https://dev.to/wardaliaqat01/cicd-pipeline-hands-on-aws-code-pipeline-elastic-beanstalk-github-35n3\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsai-manas%2Fdiabetes_predictor_ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsai-manas%2Fdiabetes_predictor_ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsai-manas%2Fdiabetes_predictor_ml/lists"}