Predictive Human Resource Analytics for Classification of Salaries in the Field of Data Science

The rapid growth of big data has led to a lack of jobs in the field of data science, resulting in a strong need for skilled experts to fill particular tasks in this domain. In order to get the aforementioned conclusions, an examination of the prevailing employment patterns in the field of data science was conducted, using a secondary dataset. The primary dashboard offered valuable analytics information for enhancing job security and fostering career advancement. Additionally, the study highlighted the key factors that are in high demand in the field of data science, which are crucial for constructing successful models. Various data pre-processing techniques were extensively used to effectively prepare and optimise the dataset for the stated goals of human resource analytics. The primary aim of this research is to do a thorough examination of human resource elements within the domain of data science employment. The present investigation will concentrate on the notable influence of specialised skill sets and their association with salary prediction. The results of this study demonstrate a considerable degree of precision, as seen by an 88 percent success rate achieved by the Random Forest Classifier.