Diabetes-Prediction-System
Introducing our revolutionary GitHub repository, dedicated to our advanced diabetes prediction web application. This repository showcases the seamless integration of machine learning and web development, utilizing the logistic regression algorithm to provide accurate diabetes predictions. Our comprehensive dataset and well-optimized logistic regression model ensure the reliability of the predictions. The repository includes the complete source code for both the machine learning model and the web interface, empowering developers to explore, contribute, and enhance the platform. By leveraging the power of Flask for the backend and HTML/CSS for the user interface, the repository serves as a valuable resource for those interested in creating similar health prediction applications.
Project Overview: Diabetes Prediction System
1. Introduction:
The Diabetes Prediction System is a web application that allows users to predict the likelihood of diabetes based on medical data. Users can register, log in, upload medical datasets, clean data, train a machine learning model, and make predictions using the trained model.
2. Key Features:
--> User Registration and Login: Users can create accounts, log in, and log out. Passwords are securely hashed using Bcrypt.
--> Dataset Loading and Cleaning: Users can upload a diabetes dataset in CSV format. The data is then cleaned and preprocessed for training.
--> Data Splitting: The cleaned dataset is split into training and testing sets for model evaluation.
--> Model Training: A Logistic Regression model is trained using the cleaned dataset to predict diabetes outcomes.
--> User Demographics Prediction: Users can input their medical data, and the trained model predicts the likelihood of diabetes.
--> Responsive User Interface: The frontend is built using HTML templates, Bootstrap for styling, and Jinja2 for dynamic content rendering.
3. Code Structure:
--> app.py: The main application file containing the Flask app, database setup, route definitions, and user management functions.
--> models.py: Defines the User model using SQLAlchemy for user registration and authentication.
--> templates/: Contains HTML templates for different pages, including registration, login, data analysis, and user demographics prediction.
--> static/: Contains CSS files, images, and frontend scripts for styling and interactivity.
--> analysis-script.js: A frontend JavaScript script for handling data analysis and visualization.
4. How It Works:
--> Users register and log in to the system.
--> They upload a diabetes dataset in CSV format.
--> The system cleans and preprocesses the dataset.
--> The dataset is split into training and testing data.
--> A Logistic Regression model is trained using the training data.
--> Users can input their medical data for prediction.
--> The trained model predicts the likelihood of diabetes.
5. Technologies Used:
--> Flask: Web framework for creating the application.
--> SQLAlchemy: ORM for managing the database and user data.
--> Bcrypt: Hashing library for securing passwords.
--> Bootstrap: CSS framework for responsive design.
--> scikit-learn: Machine learning library for data preprocessing and model training.
--> Jinja2: Template engine for rendering dynamic content in HTML templates.
6. Usage:
--> Clone the repository using git clone <repository-link>.
--> Install dependencies using pip instalL-r requirements.txt.
--> Set up a virtual environment (optional but recommended).
--> Run the application using python app.py.
--> Access the application in your web browser at http://localhost:8080.
Acknowledgments:
This project was created by @bizzorotical-ank01 and is intended for educational purposes. This repository provides the foundation for a web application that allows users to register, log in, clean a dataset, train a diabetes prediction model, and predict outcomes based on user inputs. It integrates both front-end and back-end functionalities to create a full-fledged web application.
For more details, you can explore the code and documentation within the GitHub repository; instruction.txt.
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