Developed a machine learning model to detect diabetes using the BRFSS 2015 Diabetes Health Indicators dataset. The project involved data preprocessing, feature engineering, and training multiple models, including Logistic Regression, SVM, Random Forest, MLP and XGBoost. Parameter tuning was performed using GridSearch and KFold cross-validation. Class imbalance was addressed with undersampling techniques. Separate notebooks were created for preprocessing, training and evaluation, showcasing an end-to-end workflow for diabetes prediction.