This project uses machine learning techniques to classify whether a patient is likely to have heart disease based on clinical and demographic features. The goal is to build a predictive model that can support early diagnosis and potentially assist healthcare professionals.
The Jupyter Notebook Heart-Disease-Classification.ipynb includes:
- Data preprocessing and exploration
- Feature engineering
- Model training with various classifiers (e.g., Logistic Regression, Random Forest, etc.)
- Evaluation using metrics like accuracy, precision, recall, and F1-score
- Visualization of model performance
The original data came from the Cleveland database from UCI Machine Learning Repository.
Howevever, i downloaded it in a formatted way from Kaggle.
The original database contains 76 attributes, but here only 14 attributes will be used. Attributes (also called features) are the variables what we'll use to predict our target variable.t+Disease), Attributes containing features such as:
- Age, Sex, Chest pain type
- Resting blood pressure, Cholesterol
- Fasting blood sugar, Max heart rate
- Exercise-induced angina, ST depression, etc.
✅ The dataset is included in the Repository, link is also prvided for it.
To run this notebook, install the following Python packages:
pip install pandas numpy matplotlib seaborn scikit-learnOr use the provided requirements.txt
- Clone the repo:
git clone https://github.com/M-F-Tushar/Heart-Disease-Classification.git
cd Heart-Disease-Classification- Open the notebook:
jupyter notebook Heart-Disease-Classification.ipynb- Run all cells and follow the analysis.
Contributions are welcome! Feel free to fork the repo and submit a pull request.