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