https://github.com/nikhil-donthusaram/heartdiseaseprediction
Heart Disease Prediction App is a machine learning web application that predicts the likelihood of heart disease based on user medical inputs. Built using a Decision Tree Classifier and deployed with Streamlit for an interactive, user-friendly interface.
https://github.com/nikhil-donthusaram/heartdiseaseprediction
data-analysis descision-tree joblib jupyter-notebook machine-learning matplotlib numpy pandas python3 seaborn sklearn streamlit vscode
Last synced: 5 months ago
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Heart Disease Prediction App is a machine learning web application that predicts the likelihood of heart disease based on user medical inputs. Built using a Decision Tree Classifier and deployed with Streamlit for an interactive, user-friendly interface.
- Host: GitHub
- URL: https://github.com/nikhil-donthusaram/heartdiseaseprediction
- Owner: Nikhil-Donthusaram
- Created: 2025-06-24T12:29:19.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-06-24T12:41:40.000Z (5 months ago)
- Last Synced: 2025-06-24T13:48:17.949Z (5 months ago)
- Topics: data-analysis, descision-tree, joblib, jupyter-notebook, machine-learning, matplotlib, numpy, pandas, python3, seaborn, sklearn, streamlit, vscode
- Language: Jupyter Notebook
- Homepage:
- Size: 552 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Heart Disease Prediction App
A simple and powerful machine learning web application that predicts whether a person is likely to have heart disease based on medical parameters. Built using a Decision Tree Classifier and deployed with Streamlit.
🔗 **Live App:** [Click here to try it out](https://heartdiseaseprediction-kfpzgxs2son9ivmtyu7xj3.streamlit.app/)
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## 🧠 Technologies Used
- **Machine Learning Model**: Decision Tree Classifier (sklearn)
- **Frontend/UI**: Streamlit
- **Data Handling**: NumPy, Pandas
- **Model Saving**: Joblib
- **Deployment**: Streamlit Cloud
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## 🧾 Features
- 🚀 Predicts heart disease based on 13 medical input parameters
- 📊 Displays model accuracy (88%)
- 🖼️ Includes real human heart image in the UI
- ✅ User-friendly and mobile responsive interface
- 💻 Easy to deploy, modify, or extend
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