This repository contains a data science project built on Python for detecting if a patient is suffering from heart disease. The project utilizes various machine learning algorithms and popular Python libraries such as Pandas, NumPy, Matplotlib, SciKit-Learn, and Seaborn to preprocess the data, train predictive models, and visualize the results.
This project aims to accurately predict the presence of heart disease in patients using machine learning techniques. By analyzing a comprehensive heart disease dataset and applying various algorithms, the project provides valuable insights and predictions for early detection and diagnosis.
The following libraries were used in this project:
- Pandas: Data manipulation and preprocessing.
- NumPy: Numerical operations and array manipulation.
- Matplotlib: Data visualization and plotting.
- SciKit-Learn: Machine learning algorithms and evaluation metrics.
- Seaborn: Enhanced data visualization.
- Clone this repository to your local machine using the following command:
git clone https://github.com/your-username/your-repository.git
- Navigate to the project directory:
cd heart-disease-detection
-
Run the Python scripts and notebooks using your preferred Python IDE or Jupyter Notebook.
-
Follow the instructions provided within the scripts and notebooks to preprocess the data, train the models, and visualize the results.
The project structure is organized as follows:
├── data/
│ └── heart_disease.csv
│
├── notebooks/
│ ├── Heart-Disease-Detection.ipynb
│
├── .gitignore
├── README.md
-
The
datadirectory contains the heart disease dataset (heart_disease_dataset.csv). -
The
notebooksdirectory includes Jupyter notebooks with step-by-step instructions for data preprocessing, model training, and result visualization.
Contributions to this project are welcome. If you have suggestions for improvements or would like to add new features, please feel free to submit a pull request.
The project is licensed under the MIT License.