This project is a Diabetes Prediction System built using the Support Vector Machine (SVM) algorithm. It aims to predict the likelihood of a person having diabetes based on certain input features.
To use the Diabetes Prediction System, follow these steps:
- Clone the repository to your local machine:
git clone https://github.com/yourusername/diabetes-prediction-system.git- Navigate to the project directory:
cd diabetes-prediction-system- Install the required dependencies. It's recommended to use a virtual environment:
pip install -r requirements.txt- Run the Streamlit web application:
streamlit run web_app.py-
Once the server starts, a new browser window or tab will automatically open with the Diabetes Prediction System interface.
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Input the required features (e.g. Glucose Level, Blood Pressure, BMI) into the provided fields.
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Click on the "Predict" button to obtain the prediction result.
The dataset is provided in the diabetes.csv file where a diabetic person is classified as 1 and a non diabetic person is classified as 0.
Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for classification or regression tasks. In this project, SVM is utilized for binary classification to predict whether a person has diabetes or not based on input features.
