• Pratibha Kulkarni
• Ms. V. Lavanya

This study presents a comprehensive comparison of the performance of various machine learning algorithms on structured datasets, evaluating their accuracy, computational efficiency, memory usage, and scalability. The analysis focuses on five prominent algorithms: Decision Trees, Support Vector Machines (SVM), Random Forests, Gradient Boosting, and Logistic Regression. These algorithms are widely used in diverse domains, including healthcare, finance, and e-commerce, where accurate predictions and efficient processing are crucial. The study's primary objective is to inform algorithm selection for specific tasks by examining the strengths and weaknesses of each algorithm. This involves assessing their performance on various structured datasets, identifying the most suitable algorithms for different domains, and providing guidelines for hyperparameter tuning to optimize model performance. A key aspect of the study is the examination of hyperparameter tuning's influence on model performance. Hyperparameters are critical components of machine learning algorithms, and their tuning can significantly impact the accuracy and efficiency of the models. The study provides recommendations for choosing suitable algorithms for specific tasks, considering the trade-offs between accuracy, computational efficiency, memory usage, and scalability.

Machine Learning, Structured Data, Decision Trees, Support Vector Machines, Random Forests, Gradient Boosting, Logistic Regression, Hyperparameter Tuning.

"A Comparative Study of Machine Learning Algorithms on Structured Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 9, page no.d156-d159, September-2024, Available :http://www.jetir.org/papers/JETIR2409318.pdf

"A Comparative Study of Machine Learning Algorithms on Structured Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppd156-d159, September-2024, Available at : http://www.jetir.org/papers/JETIR2409318.pdf