Early Stage Diabetes Prediction Using Ensemble Learning

In this project, we aim to predict early-stage diabetes using ensemble learning techniques. We’ve utilized a dataset comprising 520 samples with 16 features, containing valuable data on the signs and symptoms of diabetic patients. By employing ensemble learning, we can leverage the power of multiple algorithms to create a robust and accurate predictive model for identifying early-stage diabetes. The dataset will undergo cleaning and preprocessing. Then, we split the data into training and test sets, scale the features, and train an ensemble model using algorithms like random forest, gradient boosting, and AdaBoosting. Model evaluation involves accuracy calculation, visualization of results, and a confusion matrix to assess performance.