Predictive Health Analytics for Diabetic Risk Assessment and Personalized Reporting WebApp using Streamlit

Table of Contents

• Overview
• Aim
• Mission
• Technologies Used
• Installation
• Team

Overview

This project aims to predict the risk of diabetes in individuals based on various features such as pregnancies, insulin level, age, and BMI. The dataset used for this project is sourced from Kaggle, originally provided by the National Institute of Diabetes and Digestive and Kidney Diseases.

Aim

To develop a predictive health analytics tool for assessing diabetic risk and providing personalized reports.

Mission

To leverage machine learning for early detection of diabetes, enabling timely medical intervention and improving health outcomes.

Learning Objective

• Understand the end-to-end process of developing a machine learning model.
• Gain experience in deploying applications on cloud platforms like Heroku.
• Learn to build interactive web applications using Streamlit.

Technical Aspect

• Training a machine learning model using scikit-learn.
• Building and hosting a Strealit web app on Heroku.
• User input for features such as pregnancies, insulin level, age, BMI, etc., followed by a prediction display.

Technologies Used

• Python
• scikit-learn
• strealit
• seaborn
• Heroku

Installation

1. Clone this repository and unzip it.
2. Navigate into the project directory.

   cd filename

3. Create a virtual environment with Python 3 and activate it.

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

4. Install the required packages.

   pip install -r requirements.txt

5. Run Execute the following command to start the application:

python app.py

Contributors

• Bharath
• Pooja Chinta
• Yenuganti Sai Kumar

Credits

This repository was created with ❤️ by Sudarsanam Bharath.