Heart-Disease-Prediction

This project aims to predict heart disease using the UCI dataset based on clinical features like age, blood pressure, cholesterol, and chest pain. The goal is to build a machine learning model to support early diagnosis by detecting patterns linked to heart disease.

Mainly implementing and comparing three supervised learning models: Logistic Regression, Decision Tree, and Random Forest. These models are trained on the dataset and evaluated using accuracy, precision, recall, and F1 score

In the field of Healthcare, FN ( False Negatives ) category is the most harmful prediction because these can be fatal therefore weights can be assigned to the respective metrices.

The dataset is sourced from the UCI Machine Learning Repository.( https://archive.ics.uci.edu/ )

📖 Academic Context:

⚙️ Clone and Run

git clone https://github.com/Sneha-mav/Heart-Disease-Prediction.git
cd heart-disease-prediction

Install dependencies:

pip install pandas numpy scikit-learn matplotlib seaborn