Abstract
Abstract: This project addresses the critical issue of cardiovascular disease by employing machine learning techniques for early heart disease prediction. The dataset encompasses diverse patient attributes, including age, gender, blood pressure, and cholesterol levels. Our objectives include data preparation and cleaning, followed by exploratory data analysis to understand the heart disease distribution and feature relationships. The dataset is then split into training and testing sets, with models trained using Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest algorithms. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using KNN and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc.
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