Authors - Hardik I. Patel, Dharmendra Patel Abstract - Higher education institutions' reputation, financing, and student achievement are all impacted by student retention, which has grown to be a major problem. In order to properly identify at-risk students, traditional methods to retention issues frequently lack the predictive capacity and flexibility required. In order to predict student retention rates, this study makes use of machine learning approaches, giving academic leaders useful information. The suggested approach builds a strong prediction model by combining a variety of information, such as financial, behavioral, academic, and demographic factors. The model finds important patterns and trends related to retention outcomes by using sophisticated techniques like gradient boosting and neural networks. A methodical procedure that includes feature selection, data preparation, and model assessment guarantees excellent accuracy and scalability. By employing Explainable AI technologies to make forecasts clear and actionable, the study also highlights the significance of interpretability. This method allows institutions to apply timely interventions, such academic help, counseling, or financial aid changes, by turning raw data into useful forecasts. The results show how predictive analytics has the power to transform retention tactics and promote an inclusive and effective educational system. This study offers a guide for incorporating machine learning into higher education's strategic decision-making process.
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