In this project, I performed various analyses on health data, created visualizations, and trained machine learning models to classify the data. I started by loading and examining the health data, checking for and cleaning any missing values. Using Seaborn, I visualized the data to understand the relationships and distributions within it. I then separated the features and target variable, split the data into training and test sets, and scaled the features. I trained a Random Forest classifier and evaluated its performance using classification reports and confusion matrices. Finally, I identified and visualized the importance of each feature in the model. This project aimed to identify key health indicators and create a machine learning model capable of predicting individuals' health statuses.