This project aims to predict the presence of heart disease in a patient based on various health parameters using Logistic Regression. The goal is to build a model that can accurately identify whether a patient has heart disease, helping healthcare providers make better decisions.
The dataset used for this project is sourced from Kaggle/UCI Machine Learning Repository. It contains 303 records with 14 attributes such as age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise induced angina, oldpeak, the slope of the peak exercise ST segment, number of major vessels, and a few others.
Python Streamlit scikit-learn
The logistic regression model is used to classify the presence of heart disease based on the provided health parameters. The model is trained using the LogisticRegression class from the scikit-learn library.
The model achieves an accuracy of approximately 85% on the test set.
Contributions are welcome! Please fork this repository and create a pull request with your changes. Ensure your code follows the project's coding standards and include relevant tests.
The application is deployed using Streamlit.
You can access it here = https://ml-project-9-heart-disease-prediction-gaareqnad5fcvepx7l5buy.streamlit.app/
Improved one : https://ml-project-9-heart-disease-prediction-webapp-cypfdetyzlkwvesk8.streamlit.app/
If you have any questions or suggestions, feel free to contact me at prachetpandav283@gmail.com .