Driven and detail-focused professional with robust expertise in cloud computing, artificial intelligence, and data analytics. Pursuing opportunities to leverage technical and analytical skills in dynamic environments while fostering continuous learning and contributing to innovative, technology-driven solutions.
Conducted a comprehensive study using Python to evaluate the performance of various machine learning classification algorithms-such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine-on diabetes prediction. Utilized the Pima Indian Diabetes dataset, performed data preprocessing, and applied model evaluation metrics including accuracy, precision, recall, and F1-score. The research highlights the strengths and limitations of each algorithm in the context of medical diagnostics, contributing insights into selecting appropriate models for health-related applications., Comparative Analysis of Classification Algorithms for Diabetes Detection