Predicting Heart Disease Using Machine Learning

This project involves designing and developing a machine learning model to predict heart disease using Python and libraries such as Scikit-learn, Pandas, and NumPy. The model analyzes patient health data and identifies key features to accurately predict the likelihood of heart disease.

Techniques like Logistic Regression, Random Forest, and Support Vector Machines (SVM) were utilized for data analysis and classification. The project also includes preprocessing steps such as data cleaning, handling missing values, scaling, and splitting the dataset into training and testing sets.

The model's performance was evaluated using metrics like accuracy, F1-score, and a confusion matrix. This project aims to assist healthcare professionals and institutions in making data-driven decisions to assess patients' risks of developing heart disease.