Exploring the intersection of research, technology, and data science
A comprehensive end-to-end project demonstrating how to develop, tune, and deploy a Random Forest classifier for medical diagnosis using the Wisconsin Breast Cancer dataset. Achieved 97.37% accuracy with complete code walkthrough.
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From data understanding to deployment - A comprehensive 5-phase approach to machine learning with the classic Titanic survival prediction challenge, including feature engineering, model comparison, and Tkinter application deployment.
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A complete guide to implementing a Convolutional Neural Network for image classification using PyTorch, featuring data augmentation, TensorBoard visualization, and desktop deployment.
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