I am a computer science student with a focus on applied machine learning, statistical modeling, and real-world data analysis. This blog documents my exploration of publicly available datasets, model interpretability techniques, and benchmark comparisons across algorithms.

My work emphasizes clarity, reproducibility, and rigorous evaluation. Projects are selected to reflect a progression of complexity—from foundational classification tasks to advanced regression, ensemble methods, and structured model tuning.

Key areas of interest include:

• Model generalization and performance evaluation
• Practical trade-offs between algorithmic complexity and interpretability
• Data preprocessing and feature engineering pipelines
• Comparative studies across logistic regression, decision trees, random forests, and gradient boosting models

Each post is designed not just as a demonstration of implementation, but as a critical reflection on method selection, metric choice, and modeling strategy. I use this space to engage with core ML concepts and sharpen my ability to communicate technical reasoning.

For academic collaboration, dataset recommendations, or independent research inquiries, I can be contacted at:

pandyajanhvi00@gmail.com

GitHub: https://github.com/Janhvi-Pandya