Skip to content

A web application that predicts a person's diabetes status using a machine learning model. Built with Django and trained on the Pima Indian Diabetes dataset.

Notifications You must be signed in to change notification settings

malavika-suresh/Diabetes-prediction-using-Machine-Learning-Django

Folders and files

NameName
Last commit message
Last commit date

Latest commit

Β 

History

5 Commits
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 

Repository files navigation

🩺 Diabetes Prediction using Machine Learning and Django

This project predicts the likelihood of diabetes in a person based on health parameters using a machine learning model integrated with a Django web application.

πŸš€ Features

  • βœ… Predicts diabetes status using input medical data
  • 🧠 Machine Learning model trained on the Pima Indian Diabetes dataset (diabetes.csv)
  • 🌐 User-friendly web interface built with Django
  • πŸ“ Organized folder structure with separate modules for the ML model, app views, and static assets

πŸ“ Project Structure

β”œβ”€β”€ DiabetesPrediction # Django project directory

β”œβ”€β”€ app # Django app with views, templates, and forms

β”œβ”€β”€ static # Static files (CSS, JS)

β”œβ”€β”€ diabetes.csv # Dataset used for training the model

β”œβ”€β”€ manage.py # Django management script

└── README.md # Project description

πŸ› οΈ Getting Started

  1. πŸ“₯ Clone the repository
  2. 🧩 Install dependencies
  3. πŸ”§ Run migrations
  4. ▢️ Start the server
  5. 🌐 Access the web interface at http://127.0.0.1:8000/

πŸ“Š Dataset

This project uses the Pima Indian Diabetes Dataset, which includes features such as:

  • Pregnancies
  • Glucose
  • Blood Pressure
  • Skin Thickness
  • Insulin
  • BMI
  • Diabetes Pedigree Function
  • Age

πŸ“Œ Note

Ensure all dependencies (like scikit-learn, pandas, Django, etc.) are installed in your environment.

About

A web application that predicts a person's diabetes status using a machine learning model. Built with Django and trained on the Pima Indian Diabetes dataset.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published