This project uses machine learning to analyze healthcare data and predict patient outcomes based on various factors such as physical and mental health, sleep issues, and demographics.

Key Highlights:

• Dataset: Includes 714 entries with features like health assessments, sleep disturbances, and demographic information.
• Models Used: Logistic Regression, Random Forest, SVM, and Decision Tree.
• Techniques: SMOTE for class imbalance, MinMaxScaler for feature scaling, and Bayesian optimization for hyperparameter tuning.
• Results: Models show moderate accuracy, with room for improvement through advanced feature engineering and model selection.

Tools and Libraries: Python, Scikit-learn, Imbalanced-learn, Scikit-optimize, Pandas, NumPy, Matplotlib, and Seaborn.