Diabetes Prediction Using Machine Learning This project uses machine learning to predict the likelihood of a person having diabetes based on their health factors. The dataset used in this project is the "diabetes.csv" file, which contains information on various health factors such as glucose levels, blood pressure, BMI, and age, among others.
To run this project, you will need to have the following libraries installed:
To get started, clone this repository and navigate to the project directory. Then, run the following command to install the required libraries:
Copy code pip install -r requirements.txt After installing the dependencies, run the following command to run the project:
Copy code python diabetes_prediction.py This will run the linear regression model on the diabetes dataset and output various metrics such as mean squared error and R-squared values.
The model's performance on the test set is evaluated using various metrics, and the results are displayed in the output. Additionally, the seaborn library is used to visualize the data and gain insights into the relationships between different health factors and the outcome of diabetes.
If you would like to contribute to this project, please fork the repository and submit a pull request.