PREDICTING STUDENT RESULTS BASED ON STUDY HOURS USING MACHINE LEARNING

K.Balakrishna Maruthiram; Dr.G. VENKATARAMIREDDY; MOHD KHIZAR AHMED

PREDICTING STUDENT RESULTS BASED ON STUDY HOURS USING MACHINE LEARNING

Authors: K.Balakrishna Maruthiram, Dr.G. VENKATARAMIREDDY, MOHD KHIZAR AHMED

Unique Paper ID: 166525
Volume: 11
Issue: 2
PageNo: 1006-1011

Keywords: No Keywords Found

Abstract:
Data science and machine learning have shown to be extremely important and effective over time in a number of industries, including education. Computing systems are capable of learning from data and drawing conclusions thanks to machine learning, a subset of artificial intelligence. Assessment systems that forecast student achievement by assessing educational data with data mining and machine learning techniques have been introduced by recent developments in the education sector. Evaluation of student performance is an important educational metric that affects institution accreditation. Universities should use counselling to create performance improvement plans for underachievers in order to solve this. Forecasting academic achievement has emerged as a critical goal for numerous educational establishments. Helping at-risk students, making sure they stay in school, offering excellent learning materials, and improving the university's standing and reputation all depend on this. Small to medium-sized universities may find it difficult to accomplish this, particularly if they concentrate on graduate and postgraduate programs and have a dearth of student data available for research. This project's main goal is to show that it is feasible to train and model a tiny dataset and produce a prediction model with a reasonable level of accuracy. This study also looks at how visualization and clustering methods can be used to find important signs in a limited dataset. To find the most accurate model, many machine learning algorithms were trained with the best indicators. The findings showed that key indicators in tiny datasets can be successfully identified using clustering techniques

Download article

email to a friend

Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 2
PageNo: 1006-1011

PREDICTING STUDENT RESULTS BASED ON STUDY HOURS USING MACHINE LEARNING

Available:https://ijirt.org/Article?manuscript=166525

08 Jul, 2024

REAL VS AI GENERATED IMAGE DETECTION AND CLASSIFICATION

02 Jul, 2024

Secure and Efficient Outsourced Clustering using k-Means with Fully Homomorphic Encryption by Ciphertext Packing Technique

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry