This project conducts an in-depth analysis of Metabolic Syndrome, a complex medical condition associated with cardiovascular diseases and type 2 diabetes. Using Python, alongside machine learning and deep learning techniques, the study aims to derive insights from a dataset of individuals with Metabolic Syndrome. Key to this project is the Exploratory Data Analysis (EDA) to understand the data's characteristics before applying predictive modeling.

• Comprehensive EDA: To identify patterns, anomalies, correlations, and trends.
• Machine Learning and Deep Learning Techniques: For advanced data analysis.
• Optimal Model Performance with CatTreeClassifier: Highlighting its suitability for complex datasets.

• Python
• Machine Learning Libraries (e.g., scikit-learn)
• Deep Learning Libraries (e.g., TensorFlow, Keras)
• Data Analysis and Visualization Libraries (e.g., Pandas, NumPy, Matplotlib, Seaborn)

The dataset includes a variety of features such as demographic details, clinical markers, and laboratory measurements, such as age, gender, marital status, income level, race, waist circumference, BMI, and Albuminuria.

EDA provided valuable insights into the data, revealing critical relationships between various factors and Metabolic Syndrome. The CatTreeClassifier yielded the most accurate predictions, demonstrating its effectiveness in complex health data analysis.

This project demonstrates the importance of EDA in understanding medical datasets and showcases the potential of machine learning and deep learning in deriving meaningful insights from health data, particularly in relation to Metabolic Syndrome.