A Study of Challenges and Limitations to Applying Machine Learning to Highly Unstructured Data

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Abstract

In recent years, there has been a lot of curiosity about the use of machine learning algorithms to analyze unstructured data, including social media posts and videos. The limitations and difficulties posed by these technologies must be overcome in order to guarantee accurate and trustworthy results, despite the promise they hold. This study examines the restrictions and difficulties associated with using machine learning algorithms on unstructured data. We identify the main difficulties in working with unstructured data, such as the lack of standardization and the difficulty in handling noise and bias, through a literature review and case studies. We then look at methods for overcoming these obstacles, such as feature engineering, pre-processing techniques, and ensemble methods. The results of this study have implications for both theoretical and applied work in machine learning, particularly in the fields of feature engineering, model selection, and data cleaning. This paper's goal is to provide a thorough understanding of the constraints and difficulties associated with using machine learning algorithms on unstructured data, as well as to contribute to the creation of best practices for handling these data type

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