Abstract
Heart Disease Prediction is an open-source AI & Machine Learning project. Predict the risk factors for heart disease. This dataset provides information on the risk factors for heart disease. It is built using Jupyter Notebook, Machine Learning. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
This dataset provides information on the risk factors for heart disease.
The system uses 15 medical parameters such as age, sex, blood pressure, cholesterol, and obesity for prediction.
2. Objective
Predict the risk factors for heart disease.
This project demonstrates how Jupyter Notebook, Machine Learning can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
- pip for dependencies
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/sagnikghoshcr7/Heart-Disease-Prediction.git
cd Heart-Disease-Prediction- get the code from the repository and run the following command
- install required python packages if previously not installed
- Finally run on Jupyter Notebook and enjoy
git clone https://github.com/sagnikghoshcr7/Heart-Disease-Prediction.gitFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by sagnikghoshcr7 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
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