Predicting reaction based on customer's transaction using machine learning approaches

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 1, February 2023, pp. 1086~1096
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i1.pp1086-1096  1086
Journal homepage: http://ijece.iaescore.com
Predicting reaction based on customer's transaction using
machine learning approaches
Israa M. Hayder1
, Ghazwan Abdul Nabi Al Ali2,3
, Hussain A. Younis2,4
1
Department of Computer Systems Techniques, Qurna Technique Institute, Qurna, Iraq
2
School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia
3
Department of Computer Science (Educational Science), University of Basrah, Basrah, Iraq
4
College of Education for Women, University of Basrah, Basrah, Iraq
Article Info ABSTRACT
Article history:
Received Jan 20, 2022
Revised Aug 9, 2022
Accepted Sep 5, 2022
Banking advertisements are important because they help target specific
customers on subscribing to their packages or other deals by giving their
current customers more fixed-term deposit offers. This is done through
promotional advertisements on the Internet or media pages, and this task is
the responsibility of the shopping department. In order to build a relationship
with them, offer them the best deals, and be appropriate for the client with
the company's assurance to recover these deposits, many banks or
telecommunications firms store the data of their customers. The Portuguese
bank increases its sales by establishing a relationship with its customers.
This study proposes creating a prediction model using machine learning
algorithms, to see how the customer reacts to subscribe to those fixed-term
deposits or offers made with the aid of their past record. This classification is
binary, i.e., the prediction of whether or not a customer will embrace these
offers. Four classifiers that include k-nearest neighbor (k-NN) algorithm,
decision tree, naive Bayes, and support vector machines (SVM) were used,
and the best result was obtained from the classifier decision tree with an
accuracy of 91% and the other classifier SVM with an accuracy of 89%.
Keywords:
Bank marketing
Decision tree
K-nearest neighbors’ algorithm
Naive Bayes
Support vector machines
This is an open access article under the CC BY-SA license.
Corresponding Author:
Hussain A. Younis
School of Computer Sciences, Universiti Sains Malaysia
11800 USM, Penang, Malaysia
Email: hussain.younis@uobasrah.edu.iq
1. INTRODUCTION
Banking advertising comprises advertisements by financial institutions. This category includes, in
addition to advertising directed at bank clients, business reports and information pamphlets; statements about
the payment of new shares, reports on investment program outcomes, as well as several additional financial
announcements may also be included [1]–[3]. Many banks or telecommunication companies store their
customers' data to establish a relationship with customers and provide them with the best offers and at the same
time be appropriate for the customer with the guarantee that the company will recover their deposits. The
Portuguese bank increases its sales by establishing a cordial relationship with its customers. Transaction
predictions use k-nearest neighbors’ (k-NN) algorithm [4], [5], decision tree [6]–[10], naive Bayes [6],
[11]–[13], and support vector machines (SVM) [14] to bank marketing. This study proposes creating a
prediction model using machine learning algorithms to see how the customer reacts to subscribing to those
fixed-term deposits or offers made through their past data [15]–[19]. This classification is binary, i.e., the
prediction of whether or not a customer will participate in these offers. Four classifiers k-NN, decision tree,
SVM, and naive Bayes were used.

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Predicting reaction based on customer's transaction using machine learning approaches (Israa M. Hayder)
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2. LITERATURE REVIEW
As the number of Internet users and businesses grows, many clusters of e-commerce applications
appear not to be physically linked to each other in the system but are inter-related in business. Online banking
has been in practice since the 1980s, when it was first introduced by four major banks in New York [20], [21].
The study of commercial financial transactions made a short-term expectation using a logistic regressive model
and a SVM model. The comparison between them concluded that the SVM model prediction was better than
100% of the logistic regressive model and 97.67%, respectively [22]. The study was based on building an
intelligent banking system on Hercules. The results of the study reached that the Hercules architecture's
intelligent online banking has greatly improved the intelligence and security of the traditional online banking
system. Finally, we summarize and analyze the intelligent online banking system's value and innovation,
looking forward to discovering the system's flaws [23]. Research was also conducted based on neural networks
for predicting the transactions by automated teller machine (ATM) [24]. Additionally, research was done on the
security flaws in online and mobile banking systems [25], banking fraud detection [26], banking apps and online
payment systems [27], a strong and secure authentication method [28], and Internet banking user behavior. It is
advised that initiatives meant to boost trust in the financial sector receive more attention. Winning customer
trust through activities such as the secure processing and transmission of highly secret data could be a helpful
step toward retaining electronic customers [29]. The dataset is downloaded from the UCI machine learning
repository and is correlated to the Portuguese banking institution’s direct marketing campaign. These campaigns
were focused on calls over the phone. More than one call to the same customer was also made to reach whether
(yes) or not (yesno) their "term deposit" product was subscribed to. There were 4 datasets with all examples
(41,188) and 20 inputs ordered by date from which bank-additional-full.csv is used (from May 2008 to
November 2010). There are 20 variables for input and 1 variable for output (desired target). The dataset
contained a variety of customer details, including age, job, marital status, education, default, housing, loan,
contact, month, day(s)-of-week, duration, campaign, prays, previous, poutcome, em.var.rate, cons.price.idx,
cons.conf.idx, euribor3m, nr. employed and one output variable y denotes whether or not the consumer
subscribed to the term deposit. Such datasets were loaded in the Python language for preprocessing to check for
any missing values and it was found there were unknown values. However, the dataset is imbalanced [30], [31].
Deep learning, data mining, a robot, a decision tree, and k-NN were among the other studies presented
[32]–[38].
3. METHOD
3.1. Pre-processing
In this step, the data needs to be processed and it is done in four steps: the first step is the drop of
unknown data. The second step is to convert features from categorical to numeric; the third step is to balance
the data, and the last step is to choose the best feature. In the beginning, we had to clean the data from the
null values. We excluded features with 330 unknown attributes, and some categorical data have been
converted to numeric.
3.1.1. Dataset unbalanced
When we are using some classification algorithms such as k-NN or naive Bayes, and so on, those
algorithms can perform poorly, or they may lead to some overfitting problems. Because we have so many
imbalanced data points in our dataset, such that the ratio of one class is 70% and the other class is 30%, the
accuracy method will be ineffective. When applying the dataset to the classifiers, we will not get high
accuracy. However, before applying classifiers, we must first process the dataset [39]. The difference
between the balanced dataset and the unbalanced dataset was significant as it showed the bad performance in
the unbalanced dataset and the low accuracy with the delay in finding the best value for K, which reached 37
with an accuracy of 0.92. Unlike the weighted data, it reached a value of K=3, with a high accuracy of 94, as
shown in Figure 1. To solve this problem, we will use two techniques: random under-sampling and the
synthetic minority oversampling technique, as shown in Figure 2.
3.2. Classifiers and test evaluation
3.2.1. Random under-sampling method
The method is processing of the minority to make it equal to the majority by sampling fakes from
some minority class randomly and then repeating this process until the two classes are equal [40]. However,
it may negatively affect the model's performance by copying the noise rows. Moreover, they are known as
"naive resampling" approaches because they make no assumptions about the data and utilize no heuristics.
This makes them easy to design and quick to execute, which is ideal for highly large and complex datasets.

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Figure 1. Difference between the balanced and the unbalanced dataset
3.2.2. Synthetic minority oversampling technique
Synthetic minority oversampling technique (SMOTE) is attempting to select a point from the
minority and then produce a new point using the k-NN method [41]. This is a sound strategy since, in terms
of feature space, new samples are quite similar to existing instances from the minority class. But necessitates
numerous calculations, as depicted in Figure 3.
Figure 2. Our dataset unbalance Figure 3. Our dataset balanced
3.2.3. Experiment to choose of approach dataset unbalanced
After applying the two techniques, it was found that the best choice was SMOTE because the results
were better in generalization and reduced overfitting in the k-NN model. Moreover, the number of samples
was sufficient for the purpose of applying this technique, as shown in Figure 4. It has been concluded that the
value of k=3 was deduced early in the first cycle, whose high accuracy is 0.93 and that the other method
achieves the value of k=11 in the last cycle, whose weak accuracy is 0.87, such that k starts from 3 to 100.
3.2.4. Features selection from original dataset
This involves choosing some features to improve performance and reduce the model's prediction
time. When employing statistically based feature selection techniques, each input variable's relationship to
the target variable is assessed, and the input variables with the most robust relationships are chosen. Although

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the choice of statistical measures depends on the data type of both the input and output variables, these
techniques can be quick and efficient. Therefore, we will review some techniques that work on choosing the
best.
Figure 4. Accuracy values for k-NN with random under-sampling vs SMOTE
Filter methods are based on calculating the score for each feature with a target and then choosing the
best score between them. It is not a learning process, but rather the search for related features with a label
prior to process learning [42]. Algorithm advantages are not computationally expensive, and they avoid
features with least effects on the target [43]–[45].
The wrapper methods create a subset of the dataset by training (machine learning module) and then
repeat this training process by adding or removing some features until finding the best combination to
achieve the aim (using a greedy algorithm to find the best combination). The common techniques in this
approach are forward selection and backward elimination [42]. Quality is an algorithm advantage, but it is
computationally costly [46]–[48].
3.2.5. Experiment features selection
Two experiments were going to be conducted. The first experiment employed filter selection to
obtain the best 14 features, yielding the results shown in Table 1. The second experiment used wrapper
methods to get the best 14 features, and we got the results as shown in Table 2. As shown in Figure 5, after
applying the k-NN model to both methods, we obtained the highest accuracy in wrapper methods.
Table 1. Filter selection
No Features
0 age
1 default
2 housing
3 loan
4 month duration
5 duration
6 campaign
7 previous
8 poutcome
9 Emp.vqr.ratec
10 cons.price.idx
11 cons.price.idx
12 Eunbor3m
13 Nr. employed

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Table 2. Wrapper methods
No Features
0 age
1 job
2 education
3 loan
4 contact
5 month
6 Day-of-week
7 duration
8 campaige
9 Emp.vqr.ratecons.conf.idx
10 Emp.var, rate
11 cons.price.idx
12 Cons. price.idx
13 Euribor3m
Figure 5. Metrics of accuracy for two methods
3.3. Classifiers and test evaluation
3.3.1. Classifiers
After applying data processing in the previous steps, in this step, we implement machine learning
algorithms, which are divided into two categories: parametric and non-parametric classifiers. This process
aims to find the best models for each of the two sections. Moreover, the best model for each type will be
selected.
3.3.2. Non-parametric classifier
It is also called “lazy teaching” as it does not use assumptions in learning. Simply, the samples
collected in training data are used [49]. The algorithms that under this technique are k-NN and decision trees.
After processing it in the previous steps, we will apply our dataset to the k-NN classifiers and decision trees,
compare the results, and choose the best algorithm between them.

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3.3.3. Parametric classifier
Linear machine learning algorithms are what metric algorithms are called, and linear regression is
frequently used in them [50]. This technique employs algorithms such as naive Bayes, SVM, and others.
Following the processing in the preceding steps, we will apply our dataset to the naive Bayes classifiers and
SVM, compare the results, and select the best algorithm among them.
3.3.4. Experiment test evaluation
After balancing the data, we have 53,258 rows, and we will experiment with separating the data 1 to
20% for the testing dataset and 80% for the training dataset. 2 to 30% for the testing dataset and 70% for the
training dataset. According to exp experience using the decision tree in Figure 6, it has been found that 20%
of testing is better than 30%.
Figure 6. Accuracy scores for values testing (20%) vs testing (30%)
4. EXPERIMENTS AND ANALYSIS
The experiment was performed with Python, which contains most of the machine learning
algorithms. Before modeling, we need a preliminary exploration of the data set (41,188 rows and
21 features). We have previously explained that the data is not balanced to a large extent,and this problem
was addressed using the SMOTE method, which was superior to the othermethods. In this step, we will build
the models both (k-NN and decision trees) for the non-parametric method, and we will try (Pace, SVM) for
the parametric and choose the best of them in the models. The k-fold (5 folds) cross-validation that gave
better results was employed in the course of conducting the study.
4.1. KMM
We obtained the best accuracy after changing the model settings k-NN, where the number of
neighbors was chosen 3, and at the same time, the weight was determined to distance. The k-NN algorithm
was chosen is brute with k-fold (5 folds) cross-validation, see Figure 7. Moreover, we got an accuracy of 93,
but also the model does not work well in class 0, and values of k equal to 3 are undesirable because it tends to
bring about bias.
4.2. Decision tree model
The decision tree algorithm was implemented and what was obtained was the highest accuracy
after changing the model's settings, where the best criterion was chosen, which was Gini and
min_samples_splitwas 2. The length of the tree was 14 with k-fold (5 folds) cross-validation. Moreover, we
got an accuracy of 92, but also the model does not work well in class 0, as shown in Figure 8.

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Figure 7. k-NN model and classification report
Figure 8. Decision tree model and classification report
4.3. Naive Bayes model
Naive Bayes model algorithm is a classification method built on the Bayes theorem and predicated
on the idea of predictor independence. It is simply said that the presence of one feature does not depend on
the existence of any other feature in the class. The naive Bayes model algorithm was implemented with
default values, and we got an accuracy of 81%, as shown in the Figure 9.
4.4. SVM model
SVM is a collection of supervised learning techniques used for outliers’ identification, regression,
and classification. The benefits of SVMs include still useful in situations where the number of dimensions
exceeds the number of samples and efficient in high-dimensional environments. SVMs have been
implemented by using the class of SVM model from Sklearn, and we got 89% accuracy by using the settings
{'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}, as shown in Figure 10.
True
label
True
label

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Figure 9. Naive Bayes model and classification report
Figure 10. SVM model and classification report
5. RESULTS
That the model is nonparametric, k-NN has 14 features and K-3 value of parameter will be testing
accuracy of 92% and score before testing 91%. The model SVM has 14 features and Kernel=’rbf value of
parameter will be tested with 89% accuracy before testing 89%. With knowledge, the type of
model is nonparametric. The model decision tree that has 14 features and a depth of 14 values of parameters
will be tested with an accuracy of 92% and a score of 91% before testing. Finally, naive Bayes with 14
features and defaults of value parameter will have a testing accuracy of 80% and a score before testing of
80%. It is a parametric type of model. Table 3 shows the results of each model and the number of features
that were used.
Table 3. Results of experiments models
Variable Features Value of parameter Testing Accuracy Score before Testing Type of model
k-NN 14 K=3 92% 91% nonparametric
SVM 14 Kernel=”rbf 89% 89% nonparametric
Decision tree 14 Depth=14 92% 91% nonparametric
Naive Bayes 14 Default 80% 80% parametric
True
label
True
label

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6. CONCLUSION
The algorithm and the model are two terms for processor and the simplification of problems. The
model k-NN has 14 features and a K-3 value of parameter will be tested with 92% accuracy. The score before
testing is 91%. Knowing that the type of model is nonparametric, the model SVM has 14 features and
Kernel=”rbf value of parameter will be tested with 89% accuracy before testing and 89% after testing. The
model decision tree that has 14 features and depth=14 value of 14 values of parameters was tested with an
accuracy of 92% and a score of 91% before testing. Finally, the naive Bayes with 14 features and value
parameter defaults will have a testing accuracy of 80% and a score before testing of 80%. It is a parametric
type of model. The results of each model and the number of features that were used. The current assignment
uses 14 features rather than the original 20 because they were identified by feature selection techniques that
improved the model's performance in terms of time speed. According to the task assigned to us, we had to
choose only two algorithms. One is parametric and the other is referred to as non-parametric. The naive
Bayes algorithm was selected as a parametric algorithm and got an accuracy of 80%. Moreover, k-NN,
SVM's, and decision tree accuracy were (92%, 89%, and 91%) respectively, and all were considered non-
parametric algorithms. However, the decision tree was selected as an alternative to the k-NN since the value
of k=3 in the k-NN is low and could be sensitive to noise and outliers. For future work, deep learning and
clustering can also be used to determine and know the loans. This work can be made in the form of an
application that can be used on smart mobile phones in general.
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 ISSN: 2088-8708
1096
BIOGRAPHIES OF AUTHORS
Israa M. Hayder is an M.Sc. in the Computer Science Department, College of
Computer Science and Information Technology, India. She received the B.Sc. degree from
Computer Science Department at Basrah University, Iraq, in 2007. She is currently a lecturer
with the Southern Technical University, Iraq. She is recently interested in artificial intelligence
data security database systems human computer interaction programming languages. She can
be contacted at israa.mh@stu.edu.
Ghazwan Abdul Nabi Al Ali received the B.S. degree in Computer Science from
Iraq, University of Basra, and the M.S. degree in Computer Science from The University of
Science Malaysia. He is currently working as a programmer at the University of Basra. His
research interests include software engineering and deep learning. He can be contacted at
ghazwan.alali@uobasrah.edu.iq.
Hussain A. Younis received the bachelor’s degrees from the University of
Basrah, Iraq, master’s degrees from the Shiats University, India. He is currently pursuing a
Ph.D. degree with the School of Computer Sciences, Universiti Sains Malaysia (USM). He is
currently a lecturer with the College of Education for Women, University of Basrah. His
research interests include artificial intelligence, electronic education, robots, image processing,
pattern recognition, QR code, biometrics, and intelligent information systems. He can be
contacted at hussain.younis@uobasrah.edu.iq.

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