Heart Disease Prediction using SVM

This project aims to predict whether a person has heart disease or not based on various attributes such as age, sex, chest pain type, blood pressure, cholesterol level, etc. The project uses a support vector machine (SVM) classifier to perform the prediction task.

Dataset

The dataset used for this project is the Heart Disease UCI dataset from the [UCI Machine Learning Repository]. The dataset contains 303 instances and 14 attributes, including the target attribute target which indicates whether the person has heart disease (1) or not (0).

Requirements & Usage

The project requires the following libraries and packages:

• Python 3.8 or higher
• NumPy
• Pandas
• Scikit-learn

You can install them using pip:

pip install pandas numpy sklearn 

To run the project, you can use the following command:

1. After installing the dependencies, you can clone this repository to your local machine using the following command:

   git clone https://github.com/Muhammad-Talha4k/Heart-Disease-Prediction.git
   
   This notebook contain the code and explanation of the task.
   

Results

The project achieves an accuracy of 82% on the test set using the SVM classifier with a linear kernel.

Contributions

We welcome contributions from the community. Feel free to suggest improvements, fixes, or new features through issues or pull requests.