Diabetes-Prediction-using-ML

Diabetes Prediction Web Application

Overview

This project implements a web application for predicting diabetes based on machine learning models. It uses a Random Forest classifier trained on a dataset containing features like age, hypertension, heart disease, gender, smoking history, BMI, HbA1c level, and blood glucose level.

Usage

1. Clone the repository: git clone https://github.com/arkajkesav/Diabetes-Prediction-using-ML.git
2. Install dependencies: pip install -r requirements.txt
3. Run the Flask application: python app.py
4. Open your web browser and go to http://127.0.0.1:5000/ to use the prediction form.

Project Structure

• app.py: Flask application for serving the web pages and making predictions.
• templates/: Folder containing HTML templates for the web pages.
  • index.html: Form for user input.
  • result.html: Page displaying the prediction result.
• diabetes_prediction_dataset.csv: Dataset used for training the machine learning model.
• requirements.txt: List of Python dependencies.

Notes

• Make sure to update the dataset file (diabetes_prediction_dataset.csv) if you have a different dataset.
• Customize the HTML templates to match your feature names and input requirements.
• Adjust the machine learning model and preprocessing as needed for your use case.

Dependencies

• Flask
• pandas
• scikit-learn
• matplotlib
• seaborn