Learning disability early warning system based on classification algorithm

Machine learning technology quantifies the educational experience of excellent teachers and assesses student learning differences and key data. Start the risk early warning of college students' learning failure, and the system actively displays the list of early warning students and suggests intervention measures. The data preprocessing technology the scikit learn framework is adopted to explore the data, feature selection, screening, filtering, data set division, sample rebalancing, and standardized processing. Valuable data can be obtained after the operation. The processed data are modeled and trained by using a machine learning classification algorithm. The data set is divided into a test set and training set by using a random forest algorithm, logistic regression algorithm, support vector machine algorithm, and AdaBoost algorithm. The accuracy, recall, F1 value, AUC value of the predicted results of the four classification algorithms are compared, and the most suitable optimal algorithm is selected for visual analysis. From various performances in the process of model training, use Matplotlib to draw and analyze, show the effect and function of the drawing, and screen out the orientation of students due to low learning effect and whether human intervention measures should be taken in time due to the prompt early warning system.