Skip to content

Latest commit

 

History

History
93 lines (73 loc) · 5.54 KB

README.md

File metadata and controls

93 lines (73 loc) · 5.54 KB
Raw

Diabetes Web App Predictor using Classification Models

Overview

Diabetes Predictor web application predicts diabetes in patients using classification models. 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%.

The 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.

Screen Recording of Diabetes predictor application deployed on AWS

Diabetes.mp4

Jupyter Notebook

  1. Logistic Regression: Notebook focusing on Logistic Regression classification.
  2. Decision Tree: Notebook dedicated to Decision Tree classification.
  3. Naive Bayes: Notebook demonstrating the implementation of Naive Bayes classification.
  4. Support Vector Machines: Notebook showcasing Support Vector Machines classification.

Technologies Used

  • Programming Language: Python
  • Libraries: Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn , Pickle, Warnings
  • Web Framework: Flask
  • Frontend: HTML, CSS
  • Deployment: AWS Elastic Beanstalk, AWS CodePipeline

Model Evaluation

Each 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.

Model Deployment

To 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.

The application is deployed on AWS Elastic Beanstalk, with continuous integration and deployment facilitated by AWS CodePipeline.

How to use

I. Notebook Usage

  • Explore the notebooks to understand the implementation and evaluation of different Classification models Note Books.

Hosted this web application using Flask and deployed on AWS

II. Flask

  1. Clone the GitHub Repository:

    • Open your terminal or command prompt.
    • Navigate to the directory where you want to clone the repository.
    • Run the following command to clone my GitHub repository: 
      git clone https://github.com/my-username/my-repo.git
    • Replace my-username with my GitHub username and my-repo with the name of my repository.

  2. Install Python:

    • 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.

  3. Create a Virtual Environment (Optional but recommended):

    • 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: 
      python -m venv venv

  4. Activate the Virtual Environment (Optional but recommended):

    • Activate the virtual environment using the appropriate command based on your operating system:
      • On Windows: 
        venv\Scripts\activate
      • On macOS and Linux: 
        source venv/bin/activate

  5. Install Required Packages:

    • Navigate to the root folder of your cloned repository.
    • Run the following command to install the required Python packages listed in the requirements.txt file: 
      pip install -r requirements.txt

  6. Run the Flask Application:

    • 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.
    • Run the application with the following command: 
      python application.py

  7. Access the Application:

    • 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.

III. AWS (ELastic Beanstalk,Code Pipeline)

  1. Fork the GitHub Repository:

    • Click the "Fork" button in the top-right corner of this repository's page.
    • This action will create a copy of my repository under your GitHub account.
    • Use this as a source for deploying application in AWS

  2. Deploy application using ELastic Beanstalk and Code Pipeline in AWS