# Student Dropout Prediction for University with High Precision and Recall
## 1 Introduction
The algorithmic approach adjusts or tunes the model's hyperparameters to increase the model's performance. However, finding the appropriate values for the hyperparameters takes a long time, and it may only work with specific machine learning models. The data approach in the preprocessing process samples the available data, which may reduce the probability of overfitting the given data and the model. However, it may have low accuracy because only a tiny portion of the data are used. To address imbalanced data using the data approach, three methods are commonly used: oversampling [3; 9], undersampling [3], and a combined approach [8]. Oversampling inflates minor class data, undersampling reduces major class data, and the combined approach balances the benefits of both. The cost-sensitive approach is a method of re-learning data by giving different weights to misclassified data by exploiting other algorithms. Although the weights can be automatically learned, they can only work with some models. Note that algorithmic and cost-sensitive approaches depend on the specific algorithm in the supervised learning. In this paper, we introduce the Student Dropout Prediction (SDP) system, which aims to enhance the precision and recall index of predicting student dropouts, providing valuable insights to academic administration and counselors. The SDP system identifies significant features through permutation importance and SHAP analysis and addresses data imbalance by utilizing a data approach. It predicts potential student dropouts by employing a hybrid model that combines the XGBoost model with the SMOTE oversampling method and the CatBoost model with the RandomOverSamplerSMOTEENN model. To further assist academic administration, the data are analyzed using a clustering method to identify distinct groups of students who require different types of support, such as mentoring, dormitory assistance, or scholarships. Between 2015 and 2021, we obtained 67,060 student records from Gyeongsang National University and identified 27 essential feature sets from the available 40 features. Additionally, by predicting the reasons for dropouts and providing department-specific guidelines, we were able to offer personalized counseling to students. The contribution of this paper is as follows:  
* We offer guidelines for designing a model based on the most recent dropout data from South Korea's Flagship National University. * We propose the SDP system, a hybrid model that enhances dropout precision and recall while more accurately identifying the \"high-risk\" group and detecting a greater number of dropouts. * To provide customized counseling to students at risk of dropping out, we employ a clustering algorithm to identify the reasons behind this tendency. These reasons are subsequently shared with counselors and departments for effective intervention. Section 2 presents the related work on predicting university dropout. The characteristics and basic statistics of the data used in this paper are described in Section 3. The proposed prediction model, the SDP (Student Dropout Prediction) system, is described in Section 4. Section 5 presents the experiment results. Section 6 discusses the applicability of the presented results and suggestions to the academic administrators. Finally, Section 7 concludes the paper.