This project delves into a real-world heart disease dataset to identify key factors that are indicative of heart conditions. The goal is to provide actionable insights that can aid healthcare providers in developing effective strategies for patient care.
- Predictor Relationships: Examines the correlation between demographic and health indicators with heart conditions.
- Visualization: Utilizes Seaborn for creating insightful figures and tables.
- Logistic Regression: Employs logistic regression to predict the likelihood of heart conditions.
- Variable Selection: Chooses predictors based on their statistical significance and logical reasoning.
- Training and Test Datasets: Assesses the model's performance on separate data subsets.
- Statistical Analysis: Highlights the importance of chosen predictors in determining heart conditions.
- Non-Technical Summary: Simplifies complex methodologies and findings for a broader audience.
- Actionable Recommendations: Provides clear guidance based on the analysis for healthcare decision-making.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
- Python 3.x
- Jupter notebook or any (.ipynb) file reader
- Clone the repository
git clone https://github.com/Abdilamir/Heart-Disease-Analysis