• Rehmah Ahmed Batki

Machine Learning has simplified the task of teaching machines manually and have worked on making them think on their own. While it is widely used and have advanced to greater heights, there is room for much more efficient systems just by choosing the right model for your data. Machine Learning Model Selection Tool web application was created and put into use in this research with the goal of helping users choose the best supervised learning algorithms for tasks involving regression and classification. The tool's design considerations, implementation specifics, and underlying technique are covered in this document. It emphasizes mainly the importance of mean R-squared values and F1 scores as assessment metrics for tasks involving regression and classification, respectively. Machine Learning Model Selection Tool helps researchers and practitioners streamline their machine learning processes by providing an effective and user-friendly solution for algorithm selection.

Machine Learning, Model Selection, R-squared, F1 score

"Machine Learning Model Selection Tool For Supervised Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.d698-d702, March-2024, Available :http://www.jetir.org/papers/JETIR2403387.pdf

"Machine Learning Model Selection Tool For Supervised Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppd698-d702, March-2024, Available at : http://www.jetir.org/papers/JETIR2403387.pdf