Supervised Learning Techniques for Diabetes Diagnosis

This project analyzes the diabetes diagnosis dataset using supervised learning techniques, including Decision Tree, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). It demonstrates the application of these methods on a real-world dataset, along with result evaluation and effective visualization of outcomes.

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The detailed analysis is documented in a Jupyter Notebook, available in my GitHub repository 'diabetes-ml.' Click the button below to access the repository and open "diabetes_analysis.ipynb".