This project implements logistic regression techniques to predict the risk of heart disease based on various health metrics and patient data. The model aims to assist healthcare professionals in early diagnosis and intervention.
To set up the project, clone the repository and install the required packages using the following commands:
git clone https://github.com/shafaq-aslam/Predicting-Heart-Disease-Risk-with-Logistic-Regression.git
cd Predicting-Heart-Disease-Risk-with-Logistic-Regression
pip install -r requirements.txtRun the Jupyter Notebook for training and evaluating the logistic regression model:
The dataset used for this project contains various health metrics, including age, cholesterol levels, blood pressure, and other relevant features that contribute to heart disease risk. Dataset
The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions.
The logistic regression model demonstrates high accuracy in predicting heart disease risk, providing valuable insights for healthcare professionals.
Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or improvements.