The modern era’s busy schedule often leads to an unhealthy lifestyle, causing anxiety and depression. This work aims to detect heart disease symptoms at an early stage to reduce the risk of heart attacks and improve performance parameters using different machine learning techniques. As now a day’s machine learning algorithm is very popular to solve real time problems in an efficient manner, mostly research uses machine learning techniques in every field like healthcare, security, and pattern recognition. In this research work, conducted experimental work using the Kaggle datasets within the Python framework. Here applied several machine learning techniques, including LR, DT, SVM, NB, and k-NN. All these classification techniques were used as binary classifiers, where the target variable had two values, 0 and 1. The simulation results indicate that the LR (Logistic Regression) demonstrated superior accuracy and performance parameter values, like accuracy and other parameters.