Student poverty state prediction method based on data excavation

Abstract

The invention discloses a student poverty state prediction method based on data excavation. The method comprises: obtaining learning data of all students at school, consumption data information, and proportion of poor students and non-poor students; dividing student data information into a training set and a test set, and proportion of poor students and non-poor students in each set is consistent with a determined proportion; using an oversampling algorithm to perform data balance on the data in the training set; using a random forest algorithm to perform model training on the training set, testing and evaluating the model by the test set, to obtain a student poor state prediction model in optimal performance; and using the student poor state prediction model to predict the poor state of the students. The method performs comprehensive examination and prediction on poor state of the students through objective data and behaviors of the students at school, so as to prevent subjectivity and randomness in student poor state evaluation. The method is scientific and practical, and can rapidly perform algorithm solution and data analysis.

Description

technical field [0001] The invention specifically relates to a data mining-based method for predicting students' poverty status. Background technique [0002] At present, with the development of my country's economy and technology and the increase in investment in higher education, more and more students from poor areas have entered university campuses to learn knowledge and serve the motherland. However, a series of necessary expenses such as tuition and living expenses during college have become a major obstacle for college students in poor areas on their way to school. [0003] In order to allow more students to study with peace of mind and not worry about tuition fees and living expenses, the country, region and various universities have launched a series of poverty-stricken funding activities, and have also established relevant policies and systems to ensure the greatest degree Students will not drop out of school due to poverty or affect their studies due to poverty. ...

AI Technical Summary

Problems solved by technology

[0005] However, the currently adopted "government + classmate + teacher" evaluation system for students' poverty status is relatively high in subjectivity and operability, which can easily lead to the occurrence of "false identification" and "hidden poverty". Supported students receive reduced support or are unable to receive support

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