This repository contains a project made for academic progression for the subject Practical Data Science (CS667) at Pace University.
Guided by: Professor Stephanie Langelend
To enhance patient care and preemptively identify at-risk individuals, the team has developed a sophisticated predictive model for heart failure, tailored specifically for the patient demographics of the Kaiser Permanente hospital chain. Utilizing state-of-the-art machine learning techniques and the rich medical datasets available, our model aims to provide clinicians with a powerful tool to assess heart failure risks, enabling timely interventions and optimizing resource allocation, ultimately driving better patient outcomes and reducing hospital readmissions.
This section provides detailed instructions on how to use the Heart Failure Prediction project.
Before you begin, ensure you have:
- Python 3.9 installed
- Required libraries: Pandas, Numpy, Matplotlib, Seaborn, Plotly (Interactive Viz), Sci-Kit Learn.
To install this project, follow these steps:
git clone https://github.com/darshjoshi/heart_failure_prediction
cd heart_failure_prediction
pip install -r requirements.txt