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A Lasso Regression-Based Prediction Method for Students' Class Grade Ranking

A prediction method and performance technology, applied in prediction, data processing applications, instruments, etc., can solve problems such as lack of accuracy and achieve the effect of increasing accuracy

Active Publication Date: 2021-07-09
HUAIYIN INSTITUTE OF TECHNOLOGY
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0018] The traditional method of predicting students' class performance rankings is to evaluate the trend of previous grades by students themselves, which lacks accuracy.

Method used

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  • A Lasso Regression-Based Prediction Method for Students' Class Grade Ranking
  • A Lasso Regression-Based Prediction Method for Students' Class Grade Ranking
  • A Lasso Regression-Based Prediction Method for Students' Class Grade Ranking

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Embodiment Construction

[0096] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0097] Such as Figure 1-4 Shown, the present invention comprises the steps:

[0098] Step 1: Perform data preprocessing on the student performance data set, use Pearson correlation-basedsimilarity to match the student performance of the same professional direction between the two grades, and obtain the student class performance ranking matching data set, specifically as figure 2 Shown:

[0099] Step 1.1: Define YEAR as the grade student performance data set, YEAR={year 1 , year 2 , year 3 , year 4}, where, year h It is the achievement data set of middle class students in grade h, year h ∈ YEAR, h ∈ [1,4];

[0100] Step 1.2: year h ={cla 1 , cla 2 ,..., cla clasum}, where clasum is year h The number of classes in the class, cla i is the student performance data set of class i, cla i ∈ year h ,i∈[1,clasum];

[0101] Step 1.3: c...

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Abstract

The invention discloses a Lasso regression-based method for predicting student class performance rankings, comprising the following steps: 1. Carrying out data preprocessing on a student performance data set, and using Pearson correlation-based similarity to classify students of the same professional direction between two grades The results are matched to obtain the student class performance ranking matching dataset; 2. Perform data preprocessing on the student performance data set and predict the preprocessed data set through Lasso regression to obtain the student class performance ranking data set; 3. The weighted value processing is performed on the student class performance ranking data set and the student class performance ranking matching data set to obtain the final student class performance ranking prediction data result set. The invention effectively predicts the class achievement ranking of the students, and improves the prediction accuracy of the class achievement ranking of the students.

Description

technical field [0001] The invention belongs to the field of data prediction, in particular to a Lasso regression-based method for predicting student class performance rankings. Background technique [0002] Today, with the rapid development of Internet technology and the explosive growth of information, network big data ushered in, making data mining technology gradually become an important research field, providing a revolutionary impetus for the field of education. How to predict valuable information from existing data through data mining technology has become an important research content. Data prediction technology in data mining has been valued by many companies. Google, Amazon, Tencent, Taobao and other companies have obtained considerable economic benefits through data prediction technology. [0003] The use of data prediction technology plays an important role in the development of modern information technology. Although the data source of data prediction is a spe...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/20
CPCG06Q10/04G06Q50/205
Inventor 朱全银唐娥邵武杰李翔唐海波高阳钱凯潘舒新瞿学新
Owner HUAIYIN INSTITUTE OF TECHNOLOGY
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