Data preprocessing techniques in machine learning

In this paper, importance of preprocessing and techniques in this field such as data cleaning, dimensionality reduction, smoothing, normalization are illustrated. During the research we mentioned some details of techniques above. However, our research includes only theoretical aspect of data preprocessing. The data preprocessing phase while arduous and time-intensive stands as the cornerstone of data science, possessing paramount significance. Neglecting the meticulous cleansing and structuring of data has the potential to undermine the integrity and efficacy of subsequent modeling endeavors.