Heart Disease Classification Project

Important Note

You may need to edit the code to correctly load and read the dataset based on your file structure and data source. Ensure the dataset is accessible and properly referenced in the code before running the project.

Overview

This project uses machine learning to classify the presence of heart disease based on medical and health-related features. The workflow includes data exploration, visualization, preprocessing, and the application of classification algorithms.

This project is implemented in Python and leverages popular libraries for data manipulation, visualization, and machine learning.

Features

• Data Exploration: Visualizing relationships between features and the target variable.
• Preprocessing: Cleaning and preparing the dataset for machine learning.
• Feature Importance: Identifying the most significant features for classification.
• Classification Models: Training and evaluating machine learning models to classify heart disease.

Prerequisites

Before running the project, ensure you have the following Python packages installed:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• jupyter

You can install the required packages using:

pip install pandas numpy matplotlib seaborn scikit-learn jupyter

How to Run

1. Clone the repository:

   git clone https://github.com/your-username/Heart-Disease-Prediction.git
   cd heart-disease-classification

2. Edit the code to ensure the dataset is correctly loaded.
3. Launch Jupyter Notebook:

   jupyter notebook heart-disease-classification-project.ipynb

4. Run the cells sequentially to reproduce the analysis and model training.

Results

The project provides insights into the dataset and evaluates the performance of the classification models. Key outputs include:

• Visualizations of feature importance.
• Crosstabs for feature-target relationships.
• Accuracy and metrics of the trained models.

Dataset

The dataset used in this project contains medical and health-related attributes to classify the presence of heart disease. Ensure you have the dataset loaded correctly for successful execution.

Contribution

Contributions are welcome! Feel free to fork this repository, submit pull requests, or report issues.

License

This project is open-source and available under the MIT License.