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https://github.com/kasraskari/heart-disease

Prediction of Heart Disease
https://github.com/kasraskari/heart-disease

heart-disease jupyter-notebook machine-learning python support-vector-machine svm

Last synced: 9 months ago
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Prediction of Heart Disease

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# Heart Disease Prediction

## Overview

This project predicts the likelihood of heart disease using machine learning models trained on clinical data. By leveraging supervised learning algorithms, the project achieves an accuracy of approximately **85-90%**, depending on the model and dataset. These results demonstrate the potential for effective early diagnosis and prevention through data-driven insights.

## Features

- Heart disease risk prediction based on patient data.
- Implementation of multiple machine learning models.
- Preprocessing and feature engineering for optimal model performance.
- Interactive interface for users to input medical data (if applicable).

## Project Structure

- **`data/`**: Contains the dataset used for training and testing.
- **`models/`**: Saved machine learning models.
- **`notebooks/`**: Jupyter notebooks for exploratory data analysis and model training.
- **`scripts/`**: Python scripts for data processing and predictions.

## Technologies Used

- **Python**
- **Pandas**, **NumPy** for data manipulation.
- **Scikit-learn**, **XGBoost**, **Random Forest**, etc., for modeling.
- **Matplotlib**, **Seaborn** for visualization.

## Acknowledgment

The dataset used in this project is sourced from Kaggle. You can access the dataset [here](https://www.kaggle.com/datasets/venkatkarthick/heartcsv).