Description
Diabetes Prediction System using Python Machine Learning
The Diabetes Prediction System is a machine learning project that aims to predict the presence of type 2 diabetes using provided data. Diabetes, also known as diabetes mellitus, is a chronic illness that affects the body’s ability to metabolize glucose or blood sugar. The project focuses on identifying individuals at risk of developing diabetes by analyzing a dataset of individuals with and without diabetes.
Various algorithms, such as Logistic Regression, Random Forest, Naive Bayes, KNN, SVM, and Decision Tree, are used in the Diabetes Prediction System to forecast and identify diabetes. The accuracy score of the prediction model is used to evaluate its performance.
The project not only benefits individuals who want to know their medical status but also provides a reliable tool for researchers and students to assist doctors in making better decisions regarding their patients’ health.
Algorithm Used for Diabetes Prediction:
- Logistic Regression
- Random Forest
- Naive Bayes
- KNN (k-nearest neighbors)
- SVM (Support Vector Machine)
- Decision Tree
Static Pages and Other Sections:
The Diabetes Prediction System project includes the following static pages:
- Home Page with a user-friendly interface
- An animated slider for image banners on the Home Page
- About Us page describing the project
- Contact Us page for inquiries
Technology Used in the Diabetes Prediction System Project:
The project is developed using the following technologies:
- HTML for page layout design
- CSS for styling and design
- JavaScript for validation tasks and animations
- Python for implementing business logic
- MySQL as the database
- Django framework for project development
Supported Operating Systems:
The Diabetes Prediction System project can be configured on the following operating systems:
- Windows: Requires Python, PIP, and Django for configuration
- Linux: Compatible with all versions of Linux
- Mac: Easily configured on Mac operating systems
