Analysis of College Students’ Employment Competitiveness Based on Binary Association Rule Extraction Algorithm

Binary association rule extraction algorithm is a machine learning method, which is used to extract rules from text data. Text data can be in any form, such as documents, news articles, or social media posts. The rules extracted by this algorithm are called associations. These associations are then used to predict future events. This paper introduces how to use binary association rule extraction algorithm to predict students’ employment competitiveness according to their performance in SAT/ACT/NAPLEX and other college entrance examinations. Use the above algorithm to extract the employment competitiveness of college students. The binary association rule extraction algorithm is applied through the following steps: Step 1: input the data into the computer and import it into the database; Step 2: Search for rules in the database and extract them; Step 3: Combine all extracted rules into a rule set; Step 4: Calculate the score of each rule set according to its accuracy, and then compare it with the scores of other rule sets.