Data mining approach to predict academic performance of students

BOHR International Journal of Smart Computing
and Information Technology
2023, Vol. 4, No. 1, pp. 39–49
DOI: 10.54646/bijscit.2023.35
www.bohrpub.com
METHODS
Data mining approach to predict academic performance
of students
Partha Ghosh*, Reet Roy, Souhardya Mandal, Manthan Chowdhary and Subhajit Bokshi
Department of Computer Science and Engineering, Government College of Engineering & Ceramic Technology, Beleghata,
Kolkata
*Correspondence:
Partha Ghosh,
parth_ghos@rediffmail.com
Received: 20 May 2023; Accepted: 12 June 2023; Published: 20 July 2023
Powerful data mining techniques are available in a variety of educational fields. Educational research is
advancing rapidly due to the vast amount of student data that can be used to create insightful patterns
related to student learning. Educational data mining is a tool that helps universities assess and identify student
performance. Well-known classification techniques have been widely used to determine student success in
data mining. A decisive and growing exploration area in educational data mining (EDM) is predicting student
academic performance. This area uses data mining and automaton learning approaches to extract data from
education repositories. According to relevant research, there are several academic performance prediction
methods aimed at improving administrative and teaching staff in academic institutions. In the put-forwarded
approach, the collected data set is preprocessed to ensure data quality and labeled student education data
is used to apply ANN classifiers, support vector classifiers, random forests, and DT Compute and train a
classifier. The achievement of the four classifications is measured by accuracy value, receiver operating curve
(ROC), F1 score, and confusion matrix scored by each model. Finally, we found that the top three algorithmic
models had an accuracy of 86–95%, an F1 score of 85–95%, and an average area under ROC curve of
OVA of 98–99.6%.
Keywords: predictive analysis, KNN, SVM, random forest, DT classifier, students’ academic performance
1. Introduction
Due to the wealth of information in educational databases,
it is difficult to anticipate student success. Malaysia today
does not have a mechanism to research and monitor student
progress and achievement. There are two main reasons for
this. First and foremost, more research needs to be done
on existing prediction techniques before determining the
best one for predicting academic performance in Malaysian
institutions. A second reason is the lack of research on
the context-specific Malaysian factors that influence student
performance in different courses. Therefore, it is proposed
to improve student performance by conducting a thorough
literature review on data mining strategies for predicting
student success.
Higher education management is very concerned with
predicting a student’s success. The purpose of this work is
to discover the variables affecting students’ performance
on final exams and to create a suitable data mining
method to forecast students’ grades in order to provide
timely and pertinent warnings to students who are at
danger. In the current study, a survey-based experimental
approach was used to create a database constructed
using primary and secondary sources. Hypothesis test
results showed that parental occupation had a significant
effect on performance prediction, while school type had
little effect on child performance. The results of this
study will help educational institutions identify at-risk
students and provide better additional education to
vulnerable students.
39

40 Ghosh et al.
1.1. Motivation
Analyzing educational data is not a new practice, but
recent developments in educational technology, such as the
development of methods to analyze the vast amounts of
data generated in education, have led to new practices.
This enthusiasm led to a series of EDM workshops as part
of a number of international academic conferences from
2000 to 2007. The first EDM research conference, now held
annually, was founded in 2008 by a group of scholars in
Montreal, Quebec, Canada.
EDM researchers launched the Journal of Educational
Data Mining in 2009 as a platform to share and publish
their findings as the field grew in popularity. To connect
and advance the discipline, EDM researchers founded the
International Educational Data Mining Consortium in 2011.
Public education data sets have made education data
mining more accessible and convenient, fueling its
growth, since the launch of public education data sets
in 2008, including the Pittsburgh Academic Science Center
(PSLC) and the National Center for Education Statistics
(NCES) Database.
Today, EDM is becoming increasingly popular to achieve
the following goals:
(a) Predict future learning behavior of students – This
goal can be achieved by developing student models
that incorporate learner characteristics, such as specific
information about learner knowledge, behavior, and
motivation to learn. It also assesses learners’ overall
learning satisfaction and user experience.
(b) Identifying or enhancing domain models –
Discovering new models and improving old models
are made possible by a multitude of EDM techniques
and applications. Examples of this include designing a
lesson sequence that best fits a student’s learning style
or displaying lesson material to engage the student.
(c) Examine the results of educational support by
learning systems.
(d) Create and use student models, EDM research,
technology, and software to enhance learning and
learners’ scientific understanding. These goals serve as
the basis for investing time and effort in development
and are the driving force behind this work.
2. Literature survey
This section examines a number of classification techniques
traditionally used by scholars to predict student academic
performance. Here, we mainly discuss his two methods. The
first is a hybrid data mining strategy (1) developed by Bindhia
K Francis and Suvanam Sasidha rBabu, and the second is
development of Hybrid Data Mining Models (HEDM) for
educational purposes by V Ganesh Karthikeyan, P Tangaraj,
and S Karthik (2).
The first article used a 2-layer approach for classifying:
first, it fed the data to the supervised learning classifiers and
then used an unsupervised learning clustering algorithm (K-
means clustering plus majority voting) in order to increase
the accuracy. The data set includes several features such as
the Demographic, Academic, Behavioral, and Extra features.
These data have been taken with regard to the interaction
of the student with the online website of 2 institutions
from Kerala. The researchers took a subset of different
features and used 4 supervised classifying algorithms: Naive
Baye’s, Decision Tree (Iterative Dichotomies 3 algorithm),
Support Vector Machine, and Neural Network (Multilayer
Perceptron) in first layer and then the K-means clustering
algorithm that groups into clusters showing high, medium,
and low performance. Therefore, they used four performance
measures of accuracy, recall, precision, and F1 score
to compare the achievement of different classifiers on
different criteria. Through various combinations of feature
and machine learning models, they finally arrived at the
conclusion that Academic and Behavioral features of students
contributed largely to the grade received by the student and
Decision Tree was the most accurate model for predicting
grades with an accuracy of 75%
The model presented in the second article combines the
strengths of the J48 classifier with Naive Baye’s classification
approach to accurately classify student performance and
draw inferences. The information from two public schools
in the Alentejo area of Portugal for the academic year 2005–
2006 was utilized in this article. In this study, Naive Baye
was used to classify the data into pass and fail lists and
then a J48 decision tree classifier was employed to further
classify the data into “pending list” and “good list., suggests
identifying student dispositions in categories such as “average
list.” Precision, Recall, F1 Score, and Accuracy are used as the
four performance criteria to assess the effectiveness of this
technique. The final accuracy achieved by Naive Baye’s + J48
Decision Tree classifier was 98.6% using the WEKA tool.
3. The proposed work
3.1. Objective and goals
The purpose of this project is to take various features of
students as input features and predict the grade of the
student based on this factor using various ML classification
techniques to classify students and help the institute to take
necessary actions and effort to improve the performance of
students of each category and support them to success.
– The core objective of the suggested study is to put
several ML classifiers into practice for estimating
learner progress.

10.54646/bijscit.2023.35 41
– We want to analyze our model’s performance using
various performance metrics.
– In-depth analysis considers the impact of all
classifiers to determine the best classifier to predict
student performance.
3.2. Data set
• The information utilized in this lesson was gathered
from two community schools in Portugal’s Alentejo
area in the academic year 2005–2006.
• The database was built from following sources:
– Paper-based reports for school
– Characteristics (the absence and three-period
grades)
– Questionnaires, which are used to supplement the
earlier data
• Characteristic Information:
Both the student-mat.csv (for a math course) and student-
por.csv (for a Portuguese language course) databases include
the same attributes:
1. School (binary: “GP” for Gabriel Pereira or “MS” for
Mousinho da Silveira)
2. Student’s gender (binary: “F” – female or “M” – male)
3. Age of the student, 3 (numerical: 15–22)
4. Address – the address of the student residence (in the
city or in the countryside, in binary format)
5. Famsize – family size (binary: “GT3” – higher than 3
or “LE3” – fewer than 3)
6 Pstatus: the parents’ status of cohabitation (binary:
“T” for “living together” or “A” for “apartment”)
7. Medu – maternity education (number: 0 – none,
1 – primary school (grade), 2 – "grades 5–9", 3 –
“secondary education” or “further education”)
8 Fedu – father’s education (count: 0 – none, 1 –
primary education (grade 4), 2 – grades 5–9, 3 –
secondary, or 4 –continuing education)
9. Mjob stands for mother’s job and means “education,”
“medical care,” “public service” (police, government,
etc.), “home,” or “other”
10. Fjob – father’s occupation (nominative: “education,”
“medical,” “public service” (government, police, etc.),
“household,” or “other”)
11. Proximity to this school choice justification
(nominally: “home,” school “call,” desired course, or
“other”)
12. Parent – the person responsible for the student
(nominative: “mother„ “father,” or “other”)
13. Travel time from house to school (in minutes: 1:
15 min, 2: 15–30 min, 3: 1 h, or 4: more than an hour)
14. Studytime – total amount of time spent studying
each week (in hours: 0: 1, 1: 2, 2: 5, 3: 5, or 4: > 10)
15. Failures – this is the total number of failures in the
previous class (number: n if 1 = n3, otherwise 4)
16. schoolsup – continuing education support (binary:
yes or no)
17. Family Education Support Program (famsup)
(binary: yes or no)
18. Paid – additional paid tuition (binary: yes or no) for
course subjects (mathematics or Portuguese)
19. After-school activities (binary: yes or no)
20. Nursery – attended a preschool (binary: yes or no)
21. Higher – intends to further their study (binary: yes
or no)
22. Access to the Internet at home (number 22) (yes or
no in binary)
23. Romantic – engaged in a romantic partnership
(binary: yes or no)
24. famrel – strength of kinship bonds (numeric: from
1 – very bad to 5 – excellent)
25. Especially after school, free time (count: 1 – very
low to 5 – very high)
26. Fun with friends on the go (number: 1 – very low to
5 – very high)
27. Darc – alcohol consumption at work (number: 1 –
very little to 5 – very high)
28. Weekend alcohol consumption statistics (number:
1 – very low to 5 – very high)
29. Health – current state of health (number: 1 – very
bad to 5 – very good)
30. Absences – total number of days absent from school
(number: 0–93). # These numbers relate to math or
Portuguese classes.
31. G1 – first-grade point (numeric: 0–20)
32. G2 is the second-class (numeric: 0–20)
33. G3 – final grade (numeric: 0–20, output target)
3.3. Methodology
The proposed model (Figure 1) is structured for the analysis
and evaluation of PIDD. In our model, we first import the
specified data set. Then, we use different data visualization
techniques:
– Histogram (to check count of student receiving final
grade for each age)
– Count plot (to compare the count of students with
different attributes)
– Box plot (to check if any outliers are present in the data)
Based on the nature of data, we perform data
preprocessing by removing the outliers in the data. Next, we
divide the data set into test and training data sets. Then we
train the data set individually on 4 different classification
algorithms:

42 Ghosh et al.
FIGURE 1 | Data flow diagram of our model.
FIGURE 2 | Count plot showing Students final grade.
– Support Vector Machine (SVM)
– K-Nearest Neighbors (KNN)
– Decision Tree Classifier (DT)
– Random Forest Classifier (RF)
We evaluate the performance of each of the algorithms.
The performance metrics used are as follows:
– Accuracy measure
– ROC score
– F1 score
Finally, we compare the analysis based on accuracy and
obtain the final results.
3.4. Data preprocessing
3.4.1. An overview of the data set
Figure 2 represents the final grade (G3) of data set vs.
count of students in each age group for whom the data
FIGURE 3 | Graph to visualize null values.
FIGURE 4 | Count plot showing male/female count.
FIGURE 5 | Count plot showing rural and urban students.
FIGURE 6 | Boxplot showing final grade (G3) vs. age.
were collected. As can be seen in the figure, there is a large
group of students whose age is evaluated as 0. Since these
students have no age record, they were replaced as 0 in order
to eliminate null.
Figure 3 shows whether or not any attribute of the data
set contains any null value. The uniform cyan color confirms
that none of the attributes has any null values.

10.54646/bijscit.2023.35 43
FIGURE 7 | Boxplot showing final grade (G3) vs. higher education.
FIGURE 8 | Working of SVM (4).
Figure 4 compares the count of female students and male
on whom the data set was made.
The number of female candidate is 208 and that of male
candidate is 187.
Figure 5 compares the residential status (rural or urban
origin) and the count of students.
The plot shows the number of urban students as 307 and
that of rural students as 88.
Figure 6 is a boxplot showing the range of final grade (G3)
received by students of a particular age and compared with
other age groups.
Figure 7 is a boxplot showing the range of final grade (G3)
received by students who opted for higher education vs. those
who did not opt for higher education.
Once the data set is collected, preprocessing techniques are
employed to enhance the data set’s quality. Data cleansing,
feature selection, data transformation, and data reduction
are all components of data preprocessing and are considered
important phases of the knowledge discovery process. The
process of transforming real-world information into a form
that can be used by a particular data mining technique
is called data preprocessing, and this is performed before
algorithms are used.
The data set used by us is a rather clean data set that has
no null values, as indicated by Figure 3. Therefore, there is
no need to filter null values and either delete or replace them
by 0. However, the data set contains various text values for
attributes, such as Parent’s Cohabitation status, Family size,
and School. Most of these attributes have a yes or no binary
answer, which we mapped to 1 and 0, respectively. However,
for attributes like Family size, we split the attribute to the
corresponding number of categories and populate the correct
category with 1 and others with 0.
3.5. Algorithms
In this work, tests were performed using classifiers such
as SVMs, ANNs, decision trees, and random forests. These
classifiers evaluate the metrics of the data set. Pattern
recognition and nonlinear function estimation problems are
solved with SVM. Training data are nonlinearly represented
in a high-dimensional feature space using support vector
machines. This helps to build a separating hyper-plane with
the widest possible margins, resulting in a nonlinear decision
boundary in the input space. A quadratic programming
problem with a global solution provides a support vector
machine solution.
3.5.1. Decision tree algorithm
By inferring straightforward decision rules from the data,
decision trees build a model or tree that anticipates the value
of a target variable (in this case, passed). For instance, based
on our data set, children with a single guardian are more
likely to drop out of school. If one of the guardian variables
is zero in this situation, a straightforward decision tree with
one node will designate the students as dropouts (3).
3.5.1.1. Advantages and disadvantages of decision tree. It
is easily imaginable. With many other algorithms, this is not
achievable. For instance, with SVM, it is practically hard for
us to imagine data in a 10-dimensional space. It is fast since
they have logarithmic run times. Although we are already
preprocessing the data in our instances, another benefit of
decision trees is that they can employ categorical data even
without formatting.
Decision tree suffers from overfitting, and the F1 scores
for both training and test data sets differ significantly. It
does not perform well if one class dominates; however in
such situation, I believe it to be 30% vs. 70%, making
them fairly equal.
In light of this discussion, we attempted using decision
trees for this specific issue because they are frequently utilized
for classification, simple to comprehend and depict, and
appropriate for our data, where the majority of the variables
are categorical.
3.5.2. SVM
SVMs are a group of classifiers that look for the best linear
separator between two classes. Although in fact most data
would not be linearly separable, SVM uses a kernel method
to add additional features to the data by combining existing
features in different ways. For instance, a group of data points
with x and y attributes might not be linearly separable in two-
dimensional space, but they might be in three-dimensional

44 Ghosh et al.
FIGURE 9 | Working of KNN (5).
FIGURE 10 | Working of random forest classifier (6).
space where x2 + y2 is the third variable. The working
principle of SVM is shown in Figure 8.
3.5.2.1. SVM: Benefits and drawbacks. It offers two
benefits. One reason they are known as support vector is that
they only employ a small subset of data to train the model.
When we have a lot of data, this strategy is more memory-
efficient. Another benefit is its robustness in situations where
the number of data points is on the order of the number of
features. It experiences the dimensionality curse less severely.
It has disadvantages too. It may be difficult to explain
how they work. The concept of high dimensionality may
be difficult to grasp. SVM also does not work when the
number of features is much bigger than data, but this is
rarely the case. I believe it is suitable in this case because it
is a widely used classification algorithm that performs best
right out of the box.
3.5.3. K-Nearest neighbors algorithm
Classification based on nearest neighbors’ algorithm is one
where no explicit model for the data is constructed. Classifier
simply stores the data and, whenever a new point comes,
classifier assigns the new point to a class based on its closest
k neighbors. The working of KNN is shown in Figure 9.
3.5.3.1. Advantages and disadvantages of K-Nearest
neighbors algorithm. Here training times are short. There
is essentially no training and no model. Therefore, it is easy
to update KNN as the new data come. It will be very easy to
explaining this model in Laymen’s terms.
Predicting each new data point takes a long time. For
each data point, we have to calculate its nearest neighbors.
Another disadvantage is the curse of dimensionality. As
the dimensions of the feature space increase, each data
point is going to represent a bigger volume of space for
which it may not be representative. We wanted to try
KNN, as it is a very simple algorithm that makes no
assumption about the data.
3.5.4. Random forest algorithm
To generate a random forest, combine N decision trees. Then,
in the subsequent stage, we make guesses for each tree in the
first stage. The working of random forest classifier is shown
in Figure 10.
3.5.4.1. Advantages and disadvantages of random forest
algorithm. Classification and regression tasks can be
performed on random forests. It can handle large data sets

10.54646/bijscit.2023.35 45
FIGURE 11 | Count plot for comparison of probability of outcome as grade X vs. All for decision tree.
FIGURE 12 | ROC OVR of the 4 grades using decision tree.
FIGURE 13 | Count plot for comparison of probability of outcome as grade X vs. all for KNN.
with high dimensions. It avoids overfitting problems and
improves model accuracy.
Regression and classification problems may
be both accomplished using random forests, but
they are not well suited for applications that
require regression.
3.6. Performance metrics used
This is an illustration of accuracy metrics including accuracy,
F-measure, and ROC (receiver operating curve) examined
for several models.
Accuracy (A) – It is the proportion of accurate predictions
to all input samples.
Accuracy (A): (TN +TP)/(TN +TP +FP +FN) (i)
F1 measure between recall and precision – It is the
harmonic mean. Better model performance is indicated by
a higher F1 score.
F1 measure (F): (2 ∗ P ∗ R) / (P + R) (ii)
Plotting the ratio of true positives (TP) to false positives
(FP) is known as ROC. Measurement of test utility is helpful.
4. Experimental results and
discussion
We have applied all the above-mentioned classifiers
on the data set one by one and have calculated the
performance metrics.
i) Decision Tree
Confusion matrix for test and train data set

46 Ghosh et al.
FIGURE 14 | ROC OVR of the 4 grades using KNN.
FIGURE 15 | Count plot for comparison of probability of outcome as grade X vs. all for SVC.
4.1. Conclusion from the train data’s
confusion matrix
Seven children in group 1 were correctly classified as such.
Of the 61 children, 59 were correctly assigned to Group 2,
while the other 2 were incorrectly assigned to Group 3.
Of the 117 third graders, 29 were correctly identified, 1
was misclassified as a second grader, and the other 2 were
misclassified as fourth graders.
Of the 115 grade 4 students, 112 were correctly identified
as grade 4 and 3 were incorrectly identified as grade 3.
4.2. Conclusion from the test data’s
confusion matrix
Three of the six grade 1 kids in the test group were accurately
categorized as being in grade 1, while the other three were
categorized as being in grade 2.
All 19 of the grade 2 kids were appropriately categorized as
being in grade 2.
The 30 third-grade pupils were divided into 29 who were
accurately identified as such, one who was misclassified
as a second grader, and three who were misclassified
as fourth graders.
A total of 38 children were in grade 4, of whom 35
were accurately identified as such, while 3 were misidentified
as being in grade 3. Count plot for comparison of
probability of outcome as grade X vs. all for decision
is shown in Figure 11. And ROC OVR (One vs.
Rest) of the 4 grades using decision tree is shown in
Figure 12.
ii) KNN

10.54646/bijscit.2023.35 47
FIGURE 16 | ROC OVR of the 4 grades using SVC.
FIGURE 17 | Count plot for comparison of probability of outcome as grade X vs. all for random forest.
FIGURE 18 | ROC OVR of the 4 grades using random forest.
confusion matrix
Four of the seven grade 1 kids in the test group were
accurately categorized as being in grade 1, while three were
appropriately categorized as being in grade 2.
Of the 61 grade 2 pupils, 54 were accurately categorized
as being in that grade, while 7 were misclassified as
being in that grade.
Of the 117 third-grade pupils, 107 were accurately
identified as such, while 2 were misclassified as second
graders and another 8 as fourth graders.
Of the 115 grade 4 pupils, 106 were categorized properly as
being in grade 4 and 9 were misclassified as being in grade 3.
TABLE 1 | Comparison between performances of different classifier.
Classifier Name F1 score Accuracy Area under ROC curve
Decision tree 0.90 0.90 0.91
K-Nearest neighbor 0.85 0.86 0.98
SVM 0.95 0.95 0.996
Random forest 0.80 0.83 0.98
confusion matrix
One of the six grade 1 pupils in the test group was
accurately categorized as in grade 1, while the other five were
categorized as in grade 2.

48 Ghosh et al.
FIGURE 19 | Comparison of performance of different classifiers.
Of the 19 kids in grade 2, 18 were appropriately identified
as such, while 1 was mistakenly placed in grade 3.
Of the 30 third-grade pupils, 28 were accurately identified
as such, while 1 was misclassified as a second grader and the
remaining 3 as a fourth grader.
Of the 38 fourth-grade pupils, 35 were accurately identified
as such, while 3 others were misidentified as such. Count plot
for comparison of probability of outcome as grade X vs. all for
KNN is shown in Figure 13. And ROC OVR of the 4 grades
using KNN is shown in Figure 14.
iii) SVM
Confusion matrix for test and train data set
confusion matrix
All seven grade 1 pupils in the test group were accurately
identified as such.
Of the 61 grade 2 kids, 60 were accurately identified as
such, while 1 was misidentified as a grade 3 student.
Of the 115 grade 4 pupils, 113 were accurately categorized
as being in grade 4, while 2 were mistakenly categorized as
being in grade 3.
confusion matrix
Of the six grade 1 pupils tested, four were
accurately categorized as in grade 1, while two were
categorized as in grade 2.
All 19 of the grade 2 children were indeed
placed in that grade.
Notably, 32 pupils in grade 3 were evaluated, of whom
29 were accurately identified as such, while 3 were
misclassified as such.
Out of the 38 grade 4 students, all were correctly classified
as grade 4. Count plot for comparison of probability of
outcome as grade X vs. all for SVC is shown in Figure 15. And
ROC OVR of the 4 grades using SVC is shown in Figure 16.
iv) Random Forest

10.54646/bijscit.2023.35 49
confusion matrix
All seven grade 1 children in the test data were misclassified
as being in grade 2.
Of the 61 grade 2 kids, 57 were accurately identified as
such, while 4 were misclassified as grade 3.
Of the 115 grade 4 pupils, 114 were accurately identified
as being in that grade, while 1 kid was misclassified as
being in grade three.
confusion matrix
All six grade 1 pupils in the test sample were appropriately
categorized as grade 2 students.
Of the 19 pupils in grade 2, 15 were accurately identified as
such, while 4 were identified as grade 3.
Of the 32 third-grade pupils, 28 were accurately identified
as such, while 4 were misclassified as fourth-grade students.
Out of the 38 grade 4 students, 36 were correctly classified
as grade 4 while 2 were classified as grade 3. Count plot for
comparison of probability of outcome as grade X vs. all for
random forest is shown in Figure 17. And ROC OVR of the
4 grades using random forest is shown in Figure 18.
Performance of each model on test data is mentioned in
Table 1.
The training set and test set accuracy for each model is
presented side by side in Figure 19.
In this work, we applied various classification techniques
on the student data set to determine the student’s final result
(i.e., Pass or Fail) and we have found that the SVC can be
a reliable model to predict student performance accurately
because it shows consistent accuracy, F1 score, and AU-ROC
curve performance.
5. Conclusion and future scope
This study suggests using data mining techniques to
forecast students’ final grades based on past performance.
The accuracy rates of four classification methods (KNN
Classifier, Support Vector Classifier, Random Forest, and DT
classifier) were compared.
Using the accuracy score, ROC, F1 score, and Confusion
Matrix calculated from each model, the performance
of all four classifiers is compared. Finally, we found
that the average area under the ROC curve for OVA
for our top 3 algorithm models is between 98 and
99.6%, with an accuracy range of 86–95% and F1 score
of 85–95%.
Other factors can be used as input variables and other
machine learning algorithms added to the modeling process
to perform future research. In addition, it is important to
make use of data mining techniques’ effectiveness to examine
students’ academic behaviors, deal with their issues, improve
the learning environment, and enable data-driven decision-
making.
Different feature selection techniques may be utilized in
the future. The data sets can also be used with a variety of
classification algorithms.
References
1. Francis BK, Babu SS. Predicting academic performance of students using
a hybrid data mining approach. J Med Syst. (2019) 43:162. doi: 10.1007/
s10916-019-1295-4
2. Karthikeyan VG, Thangaraj P, Karthik S. Towards developing hybrid
educational data mining model (HEDM) for efficient and accurate
student performance evaluation. Soft Comput. (2020) 24:18477–87. doi:
10.1007/s00500-020-05075-4
3. AfrozChakure. Decision Tree Classification. Medium (2019). Available
online at: https://medium.com/swlh/decision-tree-classification-
de64fc4d5aac (accessed July 6, 2019).
4. Javatpoint. Support Vector Machine Algorithm. Available online
at: https://www.javatpoint.com/machine-learning-support-vector-
machine-algorithm
5. AfrozChakure. K-Nearest Neighbors (KNN) Algorithm. Medium (2019).
Available online at: https://www.datacamp.com/community/tutorials/k-
nearest-neighbor-classification-scikit-learn (accessed July 6, 2019).
6. Javatpoint. Random Forest Algorithm. Available online at: https://www.
javatpoint.com/machine-learning-random-forest-algorithm

Data mining approach to predict academic performance of students

More Related Content

Similar to Data mining approach to predict academic performance of students (20)

More from BOHRInternationalJou1 (10)

Recently uploaded (20)

Data mining approach to predict academic performance of students