https://github.com/tdeepa20/heart-disease-prediction-using-ml

Machine Learning models to predict heart disease using patient medical data
https://github.com/tdeepa20/heart-disease-prediction-using-ml

datascience jupyer-notebook machinelearning numpy python sklearn

Last synced: 3 months ago
JSON representation

Machine Learning models to predict heart disease using patient medical data

• Host: GitHub
• URL: https://github.com/tdeepa20/heart-disease-prediction-using-ml
• Owner: tdeepa20
• Created: 2025-08-28T06:19:05.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-08-28T06:29:04.000Z (11 months ago)
• Last Synced: 2025-10-07T07:44:15.583Z (10 months ago)
• Topics: datascience, jupyer-notebook, machinelearning, numpy, python, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 203 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
                        Heart Disease Prediction using Machine Learning

Heart disease is one of the leading health concerns worldwide, and early detection can help reduce risks and save lives.  

This project uses **Machine Learning algorithms** to predict the likelihood of heart disease based on patient medical features.  

## Project Workflow

1. Import dataset  

2. Data preprocessing (handling missing values, scaling, encoding, outlier detection, etc.)  

3. Model building (Logistic Regression, Random Forest, etc.)  

4. Model evaluation

## Models Used

- Logistic Regression  

- Naive Bayes  

- Support Vector Machine (SVM)  

- K-Nearest Neighbors (KNN)  

- Decision Tree  

- Random Forest  

- XGBoost  

- Neural Network  

## Requirements

- Python 3.x  

- Jupyter Notebook  

- pandas, numpy, matplotlib, seaborn, scikit-learn  

---

## Insights

- Logistic Regression gave the best accuracy in this dataset(88.52%).

---