Developing a Machine Learning Model to Predict Heart Disease Risk

Develop a machine learning model to predict the risk of heart disease based on a set of clinical and lifestyle factors. The model should use a binary classification algorithm, taking into account the following features: age, sex, blood pressure, cholesterol levels, smoking status, and physical activity level. Consider the following aspects: data preprocessing, feature engineering, model selection, model training, model evaluation, and model deployment. Please deliver your model in a Python code snippet, including data loading, model training, and model evaluation. The model should achieve a minimum accuracy of 85% on a test dataset. Please ensure that the model addresses the following key questions: What are the most critical factors influencing heart disease risk? How does the model perform on different subgroups of patients? What are the implications of the model's predictions for clinical practice?