Comparing Logistic Regression and Tree Models on HR Data

This research paper offers a thorough analysis carried out within the HR department of a consulting firm with the goal of improving employee satisfaction and retention through data-driven tactics. The research explores a broad range of employee data, including job satisfaction, performance indicators, project participation, working hours, tenure, accidents, promotions, departmental links, and pay information. The HR department and any pertinent parties with an interest in the researcher's results are included as stakeholders.The study is divided into several sections that cover project design, careful data analysis, and execution tactics. Understanding the business environment, conducting a thorough data exploration, and evaluating prediction models are priorities. The research makes use of tree-based machine learning and logistic regression techniques to provide data-driven suggestions for improving employee satisfaction and retention. The study is noteworthy for the way ethical issues are intertwined throughout, highlighting how crucial it is to process data responsibly and make moral decisions. Resources are actively used to discuss moral conundrums and provide workable answers. The main objective of this study's findings is to enable firms to make well-informed decisions that promote a happy and persistent staff. It does this via a holistic approach to HR data analysis.