Machine Learning has become a pervasive technology, finding applications in diverse fields across the globe, including the healthcare industry. This transformative technology has the potential to significantly impact medical diagnostics and predictive analysis, aiding in the early detection of various conditions such as locomotor disorders, heart diseases, and many others. By accurately predicting the presence or absence of these ailments in advance, valuable insights can be provided to medical professionals, empowering them to personalize their diagnoses and treatment plans on a patient-by-patient basis, thus revolutionizing the medical field. In this paper, our primary focus lies in predicting possible heart diseases using cutting-edge Machine Learning algorithms. By leveraging the power of these algorithms, we aim to facilitate a comparative analysis of classifiers, including decision tree, K-Nearest Neighbours, Logistic Regression, Support Vector Machine, and Random Forest. Through this analysis, we seek to identify the most suitable classifier that yields the most accurate results for heart disease prediction.

Confusion Matrix, K-Nearest Neighbours, Dummies.

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