This project aimed to create a reliable and efficient cardiovascular disease (CVD) risk prediction model using the random forest algorithm. Data exploration insights and Scikit-learn capabilities contribute to accurate risk assessments, potentially assisting healthcare professionals in making informed patient care and disease prevention decision
I created a robust predictive model to assess the risk of cardiovascular disease (CVD) using the random forest algorithm. A well-structured pipeline was used in my approach, including data exploration with seaborn and Matplotlib and data preprocessing with the Scikit-learn (sklearn) library. I then curated a relevant dataset that includes critical characteristics such as age, gender, blood pressure, cholesterol levels, and other risk factors associated with CVDs. Then visualized the dataset’s attributes and investigate potential relationships between variables and the target (CVD risk) using seaborn and Matplotlib. I then used Scikit-learn’s powerful tools to perform data preprocessing tasks such as handling missing values, encoding categorical variables, and normalizing or scaling numerical attributes which ensured that the dataset was ready for the machine learning model to be trained on.