Heart Disease Prediction - Build Classifier Model

<p>This workflow demonstrate how machine learning, specifically using the <strong>Logistic Regression</strong>, can be used to predict whether a patient has heart disease. The goal is to distinguish between patients with heart disease and those without, based on various health-related attributes.</p><p></p><p>Dataset attribute:</p><ul><li><p><strong>age</strong>: Age of the patient (in year)</p></li><li><p><strong>sex</strong>: Sex of the patient (1 = male, 0 = female)</p></li><li><p>chest pain type (<strong>cp</strong>): Type of chest pain (4 values: 1 = typical angina, 2 = atypical angina, 3 = non-anginal pain, 4 = asymptomatic)</p></li><li><p>resting blood pressure (<strong>tresbps</strong>): Resting blood pressure in mm Hg</p></li><li><p>serum cholesterol (<strong>chol</strong>): Serum cholesterol in mg/dl</p></li><li><p>fasting blood sugar &gt; 120 mg/dl&nbsp; (<strong>fbs</strong>): Fasting blood sugar level &gt; 120 mg/dl (1 = true, 0 = false)</p></li><li><p>resting electrocardiographic results (<strong>restecg</strong>): Electrocardiographic results (values: 0 = normal, 1 = having ST-T wave abnormality, 2 = left ventricular hypertrophy)</p></li><li><p>maximum heart rate achieved (<strong>thalach</strong>): Maximum heart rate achieved during exercise</p></li><li><p>exercise induced angina (<strong>exang</strong>): Whether exercise induced angina (1 = yes, 0 = no)</p></li><li><p><strong>oldpeak</strong>: ST depression induced by exercise relative to rest</p></li><li><p>slope of peak exercise ST segment (<strong>slope</strong>): Slope of the peak exercise ST segment (1 = upsloping, 2 = flat, 3 = downsloping)</p></li><li><p>number of major vessels: Number of major vessels colored by fluoroscopy (0-3)</p></li><li><p><strong>thal</strong>: Thalassemia (0 = normal, 1 = fixed defect, 2 = reversible defect)</p></li><li><p><strong>target </strong>(Heart Disease): Target variable indicating heart disease (1 = disease, 0 = no disease)</p></li></ul>