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@article{174723,
        author = {MrS.Aravind and MrM.Saimon and MrM.Sathishkumar},
        title = {ADVANCE PROCEDURE FOR STUDENT’S PERFORMANCE ANALYSIS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1486-1488},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174723},
        abstract = {The project entitled as “ADVANCE PROCEDURE FOR STUDENT’S PERFORMANCE ANALYSIS”, which has been developed using python as the front ends and MYSQL as the backend. Machine Learning is a field of computer science that makes the computer to learn itself without any help of external programs. These machine learning techniques can be used to predict the output for certain student inputs. Nowadays evaluating the student performance of any organization is going to play avital role to train the students. which is difficult to predict manually. Machine learning technique to evaluate student performance. Machine learning which is subpart of Artificial Intelligence that which helps the computer to learn on own without. The proposed system aims to create a data mining technique to effectively predict students’ performance. There are several existing solution handled student dataset to predict performance, however, the systems are suffered when the data size is huge and the attributes are dynamic. The proposed system creates and introduces a new decision support system and data classifier to handle such data. This system proposes two data mining techniques to predict students’ performance. The proposed used k-EDT k-means and enhanced decision tree used for student’s performance prediction. It collects different dataset and evaluates whether the student performance will poor or good or excellent.},
        keywords = {Student Performance Evaluation, Academic Assessment, Data-Driven Analysis, Learning Analytics, Performance Metrics, Educational Data Mining, Predictive Modeling},
        month = {April},
        }