Cardiovascular Disease Risk Prediction with Random Forest

In this project, we’ll create a robust predictive model to assess the risk of cardiovascular disease (CVD) using the random forest algorithm. A well-structured pipeline will be used in our approach, including data exploration with seaborn and Matplotlib and data preprocessing with the Scikit-learn (sklearn) library.

We’ll carefully curate a relevant dataset that includes critical characteristics such as age, gender, blood pressure, cholesterol levels, and other risk factors associated with CVDs. We’ll visualize the dataset’s attributes and investigate potential relationships between variables and the target (CVD risk) using seaborn and Matplotlib. Next, we’ll use Scikit-learn’s powerful tools to perform data preprocessing tasks such as handling missing values, encoding categorical variables, and normalizing or scaling numerical attributes. This ensures that the dataset is ready for the machine learning model to be trained on.

Ultimately, our project aims to create a reliable and efficient CVD risk prediction model using the random forest algorithm. Data exploration insights and Scikit-learn capabilities will contribute to accurate risk assessments, potentially assisting healthcare professionals in making informed patient care and disease prevention decisions.