CONFERENCE

1. ICMLBDA 2023
3rd International Conference on
Machine Learning and Big Data Analytics
29-30th May 2023
Predicting Student Academic Performance using
Machine Learning: A Comparison of Classification
Algorithms
Paper ID - 216
AUTHORS
B. Naresh1 ,B V Prasanthi2 , Lakshmi Veenadhari.CH3 , Durga
Satish.Matta4 , M Durga Rao5, Mrs. I.Kali Pradeep6
1 M.Tech (CSE) Student, Vishnu Institute of Technology, Bhimavaram.
2 Assitant Professor(CSE), Vishnu Institute of Technology, Bhimavaram.
3 Assitant Professor(CSE), Vishnu Institute of Technology, Bhimavaram.
4 Assitant Professor(CSE), Vishnu Institute of Technology, Bhimavaram.
5 Assitant Professor(CSE), Vishnu Institute of Technology, Bhimavaram.
6 Assitant Professor(CSE), Vishnu Institute of Technology, Bhimavaram.

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ICMLBDA
2023 Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
Abstract:
Paper ID: 216 @ ICMLBDA 2023 29/30-05-2023
 In today's education system, predicting and classifying student academic performance has become
increasingly important.
 By understanding a student's academic performance, educators and administrators can identify
potential issues early on and provide targeted interventions to support their success.
 Machine learning algorithms are effective tools that can be used to forecast and categorize student
academic performance based on a variety of factors, such as attendance, past grades, and
demographic data.
 In this context, various classification algorithms can be used to build models that can predict student
performance and identify the elements that support academic success.

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ICMLBDA
2023 Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
Introduction:
29/30-05-2023
 In this study the comparison of the several models includes Decision Tree, Random forest, Logistic
Regression, Adaboost, K-Nearest Neighbor, Support Vector Classifier and Stochastic Gradient
Decent algorithms used for predicting the student academic performance.
 To make an informed choice, it is also crucial to test and assess the performance of numerous
algorithms using different metrics like accuracy, precision, recall, and F1-score.
 In this study I used the model accuracy to know the best model for predicting student academic
performance.
Paper ID: 216 @ ICMLBDA 2023

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ICMLBDA
2023 Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
Literature Review:
29/30-05-2023
Published
Year
Paper Title Author names Algorithms Used Observations
2023 Performance Analysis of Machine
Learning Algorithms in Prediction of
Student Academic Performance
Michael Donkor Adane ,
Joshua Kwabla Deku and Emmanuel
Kwaku Asare
C4.5 Decision tree (CDT),
Multilayer Perceptron
(MLP), Naive Bayes (NB),
and Random Forest (RF).
The Naive Bayes algorithm out
performed the MLP and CDT on
a number of ratios.
2022 Toward Predicting Student’s Academic
Performance Using Artificial Neural
Networks (ANNs)
Yahia Baashar , Gamal Alkawsi ,
Abdulsalam Mustafa , Ammar
Ahmed Alkahtani ,Yazan A.
Alsariera, Abdulrazzaq Qasem Ali ,
Wahidah Hashim and Sieh Kiong
Tiong.
Artificial Neural Networks
(ANNs)
ANN is always combined with
data analysis and data mining
approaches, enabling studies to
evaluate how well their findings
are used to assess academic
accomplishment.
2022 A survey on Prediction and Analysis of
Students Academic Performance
Using Machine Learning Technique
Ashmina Khan, Prof. K. N. Hande. Artificial Neural Networks,
Support Vector Machine, K-
Nearest Neighbour, and
Random Forests
Examined the studies that
predict the attainment of
student outcomes, irrespective
of their form
2021 Classification and prediction of
student performance data using
various machine learning algorithms
Harikumar Pallathadka , Alex
Wenda , Edwin Ramirez-Asís ,
Maximiliano Asís-López , Judith
Flores-Albornoz , Khongdet
Phasinam
Nave Bayes, ID3, C4.5, and
SVM
A comparison of the best
outcomes from Nave Bayes,
ID3, C4.5, and SVM prediction
algorithms.
Paper ID: 216 @ ICMLBDA 2023

5. 5
ICMLBDA
2023 Title: Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
Proposed Methodology:
29/30-05-2023
The proposed system predicts and categorizes the student's academic performance using the
machine learning algorithms Decision tree classification, Random Forest, Support Vector Machine (SVM),
Logistic Regression (LR), Adaboost, Stochastic Gradient Decent, and K-Nearest Neighbor.
Paper ID: 216 @ ICMLBDA 2023

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ICMLBDA
2023 Title: Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
Implementation:
29/30-05-2023
The specified machine learning classifiers are used to test the model in this step. There have been a
few models developed and their accuracy has been verified. The classifiers listed below are utilized in this
project.
1. Decision Tree(DT)
2. Random Forest(RF)
3. Support Vector Machine (SVM)
4. Logistic Regression (LR)
5. Adaboost (ADA)
6. Stochastic Gradient Decent (SGD)
7. K-Nearest Neighbor(KNN)
Paper ID: 216 @ ICMLBDA 2023

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ICMLBDA
2023 Title: Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
Dataset Description
29/30-05-2023
In our study two dataset were extracted from The UCI Machine Learning Repository those describes the
performance in two different subjects, Portuguese language (por) with 649 records and mathematics (mat)
with 395 records. Each of the dataset includes 33 characters/features of the students as columns. So, totally
for this study I used 1044 records for 33 characters of two datasets.
school sex Age
address famsize Pstatus
medu Fedu Mjob
fjob reason guardian
traveltime studytime failures
schoolsup famsup paid
activities nursery higher
internet romantic famrel
freetime goout Dalc
walc health absences
G1 G2 G3
Paper ID: 216 @ ICMLBDA 2023

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ICMLBDA
2023 Title: Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
Results and Discussion
Paper ID: 178 @ ICMLBDA 2023 29/30-05-2023
Correlation Heatmap
1. There is a strong positive correlation between study
time and grades, which suggests that students who
spend more time studying tend to have higher
grades.
2. There is a negative correlation between absenteeism
and grades, which indicates that students who miss
more classes tend to have lower grades.
3. There is a weak positive correlation between the final
grade and the level of education of the mother for
both classes, indicating that students whose mothers
have higher levels of education may have slightly
higher grades.
4. There is a weak negative correlation between the
final grade and the number of past class failures,
suggesting that students who have failed classes in
the past may have slightly lower grades.
Paper ID: 216 @ ICMLBDA 2023

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ICMLBDA
2023 Title: Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
Results and Discussion
29/30-05-2023
ACCURACY SCORE OF CLASSIFICATION ALGORITHMS
Model Name DT RF KNN LR SVC AB SGD
Model Accuracy 0.904 0.981 0.887 0.899 0.888 0.874 0.825
Cross
Validation Score
0.847 0.837 0.813 0.9 0.866 0.823 0.746
80:20
Paper ID: 216 @ ICMLBDA 2023

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ICMLBDA
2023 Title: Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
Results and Discussion
29/30-05-2023
ACCURACY SCORE OF CLASSIFICATION ALGORITHMS 70:30
Model Name DT RF KNN LR SVC AB SGD
Model Accuracy 0.895 0.982 0.856 0.882 0.874 0.860 0.850
Cross Validation
Score
0.873 0.873 0.834 0.898 0.882 0.857 0.829
Paper ID: 216 @ ICMLBDA 2023

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ICMLBDA
2023 Title: Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
Conclusion:
29/30-05-2023
Our study's findings suggest that logistic regression performs better than the other classification
algorithms at predicting students' academic performance. This is a significant finding that can have practical
implications for educators and administrators who are looking to identify students who may be at risk of poor
performance.
There are many directions for future research in the field of student performance prediction, and the
results of our study provide a strong foundation upon which to build. By continuing to refine and improve
machine learning methods for predicting student academic performance, we can help educators and
administrators identify students who may be at risk of poor performance and provide them with the support
they need to succeed.
Paper ID: 216 @ ICMLBDA 2023

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ICMLBDA
2023 Title: Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
References:
29/30-05-2023
1. Adane, Michael Donkor and Deku, Joshua Kwabla and Asare, Emmanuel Kwaku, Performance Analysis of Machine
Learning Algorithms in Prediction of Student Academic Performance (March 18, 2023). Journal of Advances in
Mathematics and Computer Science, Volume 38, Issue 5, Page 74-86, 2023; DOI: 10.9734/jamcs/2023/v38i51762,
Available at SSRN: https://ssrn.com/abstract=4392884
2. Baashar, Y.; Alkawsi, G.; Mustafa, A.; Alkahtani, A.A.; Alsariera, Y.A.; Ali, A.Q.; Hashim, W.; Tiong, S.K. Toward Predicting
Student’s Academic Performance Using Artificial Neural Networks (ANNs). Appl. Sci. 2022, 12, 1289.
https://doi.org/10.3390/app12031289
3. A survey on Prediction and Analysis of Students Academic Performance Using Machine Learning Technique Ashmina
Khan, Prof. K. N. Hande. Volume 10 Issue VI June 2022.
https://doi.org/10.22214/ijraset.2022.43192
4. Classification and prediction of student performance data using various machine learning algorithms Harikumar
Pallathadka , Alex Wenda , Edwin Ramirez-Asís, Maximiliano Asís-López ,Judith Flores-Albornoz , Khongdet Phasinam.
July 2021
https://www.researchgate.net/publication/353611098
Paper ID: 216 @ ICMLBDA 2023

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Title: Predicting Student Academic Performance using Machine Learning:A Comparison of Classification Algorithms
Thank You
naresh.bvrice@gmail.com
ICMLBDA
2023
29/30-05-2023
Paper ID: 216 @ ICMLBDA 2023