Published Paper Details:

The focus of this research lies in a thorough examination of the Classification Methods employed in Machine Learning, with special attention given to their practical applications and accompanying difficulties. Classification, being a fraction of supervised learning, involves feeding input data into predetermined goals. It has wide-ranging significance across multiple fields such as credit decision-making, medical diagnosis, and targeted marketing [1]. Different algorithms have been customized to carry out time series classification, each demonstrating varying levels of precision based on the dataset under consideration. As a result, it becomes crucial to take into account a broad range of algorithms whenever faced with a time series classification problem. To streamline this procedure, the adoption of an automated platform capable of methodically exploring algorithmic possibilities and hyperparameters is suggested, potentially leading to significant time gains, particularly during initial exploration stages [2]. Potential future development includes platforms equipped with time series classification abilities projected to emerge in the future