A Practical Guide to Support Predictive Tasks in Data Science
e Augusto C
amara Filho
, Jos
e Maria Monteiro
, C
esar Lincoln Mattos
and Juv
encio Santos Nobre
Department of Computing, Federal University of Cear
a, Fortaleza, Cear
a, Brazil
Department of Statistics and Applied Mathematics, Federal University of Cear
a, Fortaleza, Cear
a, Brazil
Practical Guide, Prediction, Data Science.
Currently, professionals from the most diverse areas of knowledge need to explore their data repositories in
order to extract knowledge and create new products or services. Several tools have been proposed in order
to facilitate the tasks involved in the Data Science lifecycle. However, such tools require their users to have
specific (and deep) knowledge in different areas of Computing and Statistics, making their use practically
unfeasible for non-specialist professionals in data science. In this paper, we propose a guideline to support
predictive tasks in data science. In addition to being useful for non-experts in Data Science, the proposed
guideline can support data scientists, data engineers or programmers which are starting to deal with predic-
tive tasks. Besides, we present a tool, called DSAdvisor, which follows the stages of the proposed guideline.
DSAdvisor aims to encourage non-expert users to build machine learning models to solve predictive tasks, ex-
tracting knowledge from their own data repositories. More specifically, DSAdvisor guides these professionals
in predictive tasks involving regression and classification.
Due to a large amount of data currently available,
arises the need for professionals of different areas to
extract knowledge from their repositories to create
new products and services. For example, cardiolo-
gists need to explore large repositories of electrocar-
diographic signals in order to predict the likelihood
of sudden death in a certain patient. Likewise, tax
auditors may want to explore their databases in or-
der to predict the likelihood of tax evasion. How-
ever, in order to build predictive models, these non-
specialist professionals need to acquire knowledge in
different areas of Computing and Statistics, making
this task practically unfeasible. Another alternative
is ask experienced data science professionals to help,
which creates dependency instead of autonomy. In
this context, the popularization of data science be-
comes an important research problem (Provost and
Fawcett, 2013).
Data science is a multidisciplinary area involv-
ing the extraction of information and knowledge from
large data repositories (Provost and Fawcett, 2013).
It deals with the data collection, integration, manage-
ment, exploration and knowledge extraction to make
decisions, understand the past and the present, pre-
dict the future, and create new services and prod-
ucts (Ozdemir, 2016). Data science makes it pos-
sible to identifying patterns hidden and obtain new
insights hidden in these datasets, from complex ma-
chine learning algorithms.
The Data Science lifecycle has six stages: busi-
ness grasp, data understanding, data preparation,
modeling, evaluation, and deployment. To extract
knowledge from the data, we must be able to (i) un-
derstand yet unsolved problems with the use of data
mining techniques, (ii) understand the data and their
interrelationships, (iii) extract a data subset, (iv) cre-
ate machine learning models in order to solve the se-
lected problem, (v) evaluate the performance of the
new models, and (vi) demonstrate how these models
can be used in decision-making (Chertchom, 2018).
The complexity of the previous tasks explains why
only highly experienced users can master the entire
Data Science lifecycle. On the other hand, several
tools have been proposed in order to support the tasks
involved in the Data Science lifecycle. However, such
tools require their users to have specific (and deep)
knowledge in different areas of Computing and Statis-
tics, making their use practically unfeasible for non-
specialist professionals in data science.
In this paper, we propose a guideline to support
Filho, J., Monteiro, J., Mattos, C. and Nobre, J.
A Practical Guide to Support Predictive Tasks in Data Science.
DOI: 10.5220/0010460202480255
In Proceedings of the 23rd International Conference on Enter prise Information Systems (ICEIS 2021) - Volume 1, pages 248-255
ISBN: 978-989-758-509-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
predictive tasks in data science. In addition to being
useful for non-experts in Data Science, the proposed
guideline can support data scientists, data engineers
or programmers which are starting to deal with pre-
dictive tasks. In addition, we present a tool, called
DSAdvisor, which following the stages of the pro-
posed guideline. DSAdvisor aims to encourage non-
expert users to build machine learning models to solve
regression or classification tasks, extracting knowl-
edge from their own data repositories. DSAdvisor
acts like an advisor for non-expert users or novice data
The rest of this paper is organized as follows. Sec-
tion 2 reviews related works. In section 3, the pro-
posed guideline is laid out. The DSAdvisor is com-
mented in section 4. Finally, in section 5 we present
our conclusions and suggestions for future research.
In this section we will discuss the main related works.
For a better understanding, we organized the related
works into two categories: supporting tools and prac-
tical guidelines.
2.1 Data Mining Tools
Traditional data mining tools help companies estab-
lish data patterns and trends by using a number of
complex algorithms and techniques. As example of
such tools, we can cite: KEEL, Knime, Orange,
RapidMiner and WEKA (Hasim and Haris, 2015).
KEEL (Knowledge Extraction based on Evolu-
tionary Learning) is a software that facilitates the
analysis of the behavior of evolutionary learning in
different approaches of learning algorithm such as
Pittsburgh, Michigan, IRL (iterative rule learning)
and GCCL (genetic cooperative-competitive learn-
ing) (Alcal
a-Fdez et al., 2009). Knime is a mod-
ular environment that enables easy integration of
new algorithms, data manipulation and visualization
methods. It allows the selection of different data
sources, data preprocessing steps, machine learning
algorithms, as well as visualization tools. To cre-
ate the workflow, the user drag some nodes, drop
onto the workbench, and link it to join the input
and output ports. The Orange tool has different
features which are visually represented by widgets
(e.g. read file, discretize, train SVM classifier, etc.).
Each widget has a short description within the in-
terface. Programming is performed by placing wid-
gets on the canvas and connecting their inputs and
outputs (Dem
sar et al., 2013). RapidMiner pro-
vides a visual and user friendly GUI environment.
This tool uses the process concept. A process may
contain subprocesses. Processes contain operators
which are represented by visual components. An
application wizard provides prebuilt workflows for
a number of common tasks including direct market-
ing, predictive maintenance, sentiment analysis, and a
statistic view which provides many statistical graphs
(Jovic et al., 2014). Weka offers four operating op-
tions: command-line interface (CLI), Explorer, Ex-
perimenter and Knowledge flow. The “Explorer” op-
tion allows the definition of data source, data prepa-
ration, run machine learning algorithms, and data vi-
sualization (Hall et al., 2009). DSAdvisor is an ad-
visor for non-expert users or novice data scientists,
which following the stages of the guideline proposed
in this paper. DSAdvisor aims to encourage non-
expert users to build machine learning models to solve
regression or classification tasks, extracting knowl-
edge from their own data repositories.
Even before the popularization of data Science,
all these tools were developed to help with data min-
ing tasks. These tools differ regarding tool usability,
type of license, the language in which they were de-
veloped, support for data understanding, and missing
values handle. The most widely used tools include
KEEL, Knime, Orange, RapidMiner, Tanagra, and
Weka. The table 1 provides a comparison between
these tools and the DSAdvisor.
In the other hand, AutoML tools enable you to au-
tomate some machine learning tasks. Although it be
important to automate all machine learning tasks, that
is not what AutoML does. Rather, it focuses on a few
repetitive tasks, such as: hyperparameter optimiza-
tion, feature selection, and model selection. Exam-
ples of these tools include: AutoKeras, Auto-WEKA,
Auto-Sklearn, DataRobot, H20 and MLBox.
2.2 Guidelines
A guideline is a roadmap determining the course of a
set of actions that make up a specific process, in addi-
tion to a set of good practices for the performance of
these activities (Dictionary, 2015). Some guidelines
have been proposed to manage general data mining
In (Melo et al., 2019), the authors presented a
practical guideline to support the specific problem of
predict change-proneness classes in oriented object
software. In addition, they applied their guideline
over a case study using a large imbalanced dataset ex-
tracted from a wide commercial software. It is im-
portant to highlight that, in this work, we extend the
A Practical Guide to Support Predictive Tasks in Data Science
Table 1: General characteristics of data mining software. Adapted from (Hasim and Haris, 2015).
Softwares list
Software Usability License Language Data Understanding Missing values handle
DSAdvisor High GPL Python Perform Intermediate
KEEL High GPL Java Perform Basic
KNIME Low Outra Java Perform Basic
RapidMiner High GPL Java Partially performs Basic
Orange Low GPL C++, Python Partially performs Basic
Weka Low GPL Java Partially performs Basic
guideline proposed in (Melo et al., 2019) to the more
general data science context.
(Luo et al., 2016) highlight the flexibility of
the emerging machine learning techniques, however,
there is uncertainty and inconsistency in the use of
such techniques. Machine learning, due to its intrin-
sic mathematical and algorithmic complexity, is often
considered “black magic” that requires a delicate bal-
ance of a large number of conflicting factors. This, to-
gether with inadequate reporting of data sources and
the modeling process, makes the research results re-
ported in many biomedical articles difficult to inter-
pret. It is not uncommon to see potentially spurious
conclusions drawn from methodologically inadequate
studies, which in turn undermines the credibility of
other valid studies and discourages many researchers
who could benefit from adopting machine learning
techniques. In the light of this, guidelines are pro-
posed for the use of predictive models in clinical set-
tings, ensuring that activities are carried out correctly
and reported.
This section describes the proposed guide to support
regression and classification tasks, which is organized
into three phases: exploratory analysis, data prepro-
cessing and building predictive models. Each one of
these phases will be detailed next.
3.1 Phase 1: Exploratory Analysis
The first phase of the proposed guideline aims to ana-
lyze a dataset, provided by the user, and next, describe
and summarize it. Figure 1 illustrates this phase,
which comprises the following activities: uploading
the data, checking the type of variables, removing
variables, choosing missing value codes, exhibiting
descriptive statistics, plotting categorical and discrete
variables, analyzing distributions, and displaying cor-
So, the guide indicates the use of different descrip-
tive statistics, such as the number of lines (count),
mean, standard deviation (std), coefficient of varia-
tion (cv), minimum (min), percentiles (25%, 50%,
75%) and maximum (max) for numerical variables,
and count, number of distinct values (unique), the
most frequent element (top) and the most common
value’s frequency (freq) for categorical variables. Be-
sides, the guideline recommend the use of different
strategies for identifying and showing missing values.
Moreover, the proposed guideline suggests dif-
ferent methods to assess the distribution of vari-
ables, such as Cram
er Von Mises (Cram
er, 1928),
D’Agostino’s K-squared (D’Agostino, 1970), Lil-
liefors (Lilliefors, 1967), Shapiro-Wilk (Shapiro and
Wilk, 1965) and Kolmogorov-Smirnov (Smirnov,
1948). All these methods serve to determine the ve-
racity of a hypothesis (Hirakata et al., 2019). In
this guide we want to state whether a variable fol-
lows (H0) or not (H1) a normal distribution, and each
method has its own way of calculating and return-
ing a result to identify the most suitable hypothesis.
Each test considers two hypotheses about the variable
under study. One is called the null hypothesis (H0),
which is assumed to be true until proven otherwise.
The second is called the alternative hypothesis (H1),
which represents a statement that the parameter of in-
terest differs from that defined in the null hypothesis,
so that the two hypotheses are complementary. The
hypotheses used by the tests are: i)H0: The variable
follows a normal distribution and ii) H1: The variable
does not follow a normal distribution.
Finally, the guide indicates the use of different
correlation coefficients based on the data distribution.
Spearman’s correlation coefficients (Spearman, 1961)
will be displayed for all pair of numerical data. If a
pair of variables (columns or features) follow a nor-
mal distribution, the Pearson’s correlation coefficients
(Pearson, 1895) must be computed and shown. If the
user dataset contains categorical data, the guide sug-
gest to display Cramer’s V. (Cram
er, 1928).
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Figure 1: Phase 1 - Exploratory Analysis.
3.2 Phase 2: Data Preprocessing
Data preprocessing is an essential component to solve
many predictive tasks. The purpose of the second
phase of the proposed guide is to prepare the data
in order to use it to build predictive models. This
phase includes activities related to outlier detection,
data normalization, choose the independent variable,
selection of attributes, data balancing, feature selec-
tion, and division of training and testing sets. Figure 2
illustrates the activities that make up the second phase
of the proposed guide.
3.2.1 Problem Setup
In this step, the predictive task type (regression or
classification) must be defined. Besides, the depen-
dent variable should be identified. Moreover, the pro-
portion of the training and test sets have to be spec-
ified. Other options for the third phase are the pre-
dictive algorithms list, score function for optimizing
GridSearchCV and metrics list for Results Presenta-
tion. The list of predictive algorithms, the list of met-
rics and the score function will be according to the
type of problem to be treated, classification’s or re-
gression’s problem.
3.2.2 Choose Normalization Techniques
Verifying that the variables are on the same scale is
an essential step at this point. For example, two vari-
ables may be expressed in different ranges, such as
integers and the interval between 0 and 1. Therefore,
Figure 2: Phase 2 - Data preprocessing.
it is necessary to normalize all variables in the dataset.
For instance, for the activation function in the neural
network it is recommended that the data be normal-
ized between 0.1 and 0.9 instead of 0 and 1 to avoid
saturation of the sigmoid function (Basheer and Ha-
jmeer, 2000). The normalization techniques used in
this guideline are z-score and min-max normalization.
A Practical Guide to Support Predictive Tasks in Data Science
3.2.3 Outlier Detection
Outliers are extreme values that deviate from other
observations on data (i.e., an observation that diverges
from an overall pattern on a sample). Detected out-
liers are candidates for aberrant data that may other-
wise adversely lead to model misspecification, biased
parameter estimation and incorrect results. It is there-
fore important to identify them prior to creating the
prediction model (Liu et al., 2004).
A survey to distinguish between univariate vs.
multivariate techniques and parametric (Statistical)
vs. nonparametric procedures was done by (Ben-
Gal, 2005). Detecting outliers is possible when multi-
variate analysis is performed and the combinations of
variables are compared with the class of data. In other
words, an instance can be a multivariate outlier but a
usual value in each feature, or it can have values that
are outliers in several features, but the whole instance
might also be a usual multivariate value (Escalante,
There are two main techniques to detect outliers:
interquartile range (a univariate parametric approach)
and adjusted boxplot (a univariate nonparametric ap-
proach). Next, we describe each one of these tech-
Interquartile Range. The interquartile range (IQR)
is a measure of statistical dispersion, often used to
detect outliers. The IQR is the length of the box
in the boxplot (i.e., Q3 - Q1). Here, outliers are
defined as instances below Q1 1.5 IQR or above
Q3 + 1.5 IQR.
Adjusted Boxplot. Note that the boxplot assumes
symmetry because we add the same amount to Q3 as
we subtract from Q1. In asymmetric distributions, the
usual boxplot typically flags many regular data points
as outlying. The skewness-adjusted boxplot corrects
that by using a robust measure of skewness in deter-
mining the fence (Hubert and Vandervieren, 2008). In
this new approach, outliers are defined as instances
such that if medcouple (MC) 0, they are below
Q1 1.5e
IQR or above Q1 + 1.5e
if not, they are below Q1 1.5e
IQR or above
Q1 + 1.5e
IQR. To measure the skewness of a
univariate sample (x
, ... , x
) from a continuous uni-
modal distribution F, we use the MC, and Q2 is the
sample median (Brys et al., 2004). It is defined as in
equation 1:
MC = med
, x
) (1)
Outliers are one of the main problems when building
a predictive model. Indeed, they cause data scientists
to achieve suboptimal results. To solve that, we need
effective methods to deal with spurious points. If it is
obvious that the outlier is due to incorrectly entered
or measured data, you should drop the outlier.
3.2.4 Feature Selection
Feature selection is referred to the process of obtain-
ing a subset from an original feature set according to
certain feature selection criterion, which selects the
relevant features of the dataset. It plays a role in com-
pressing the data processing scale, where the redun-
dant and irrelevant features are removed (Cai et al.,
2018). Feature selection technique can pre-process
learning algorithms, and good feature selection re-
sults can improve learning accuracy, reduce learning
time, simplify learning results, reduction of dimen-
sional space and removal of redundant, irrelevant or
noisy data (Ladha and Deepa, 2011).
Feature selection methods fall into three cate-
gories: filters, wrappers, and embedded/hybrid meth-
ods. Filter method takes less computational time
for selecting the best features. As the correlation
between the independent variables is not considered
while selecting the features, this leads to selection of
redundant features (Venkatesh and Anuradha, 2019).
Wrapper are brute-force feature selection methods
that exhaustively evaluate all possible combinations
of the input features to find the best subset. Embed-
ded/hybrid methods combine the advantages of both
approaches, filters and wrappers. A hybrid approach
uses both performance evaluation function of the fea-
ture subset and independent test. (Veerabhadrappa
and Rangarajan, 2010).
3.2.5 Choose Resample Techniques for
Imbalanced Data
Imbalanced data problem occurs in many real-world
datasets where the class distributions of data are asi-
metric. It is important to note that most machine
learning models work best when the number of in-
stances of each class is approximately equal (Lon-
gadge and Dongre, 2013). The imbalanced data prob-
lem causes the majority class to dominate the minor-
ity class; hence, the classifiers are more inclined to
the majority class, and their performance cannot be
reliable (Kotsiantis et al., 2006).
Many strategies have been generated to handle the
imbalanced data problem. The sampling-based ap-
proach is one of the most effective methods that can
solve this problem. The sampling-based approach
can be classified into three categories, namely: Over-
Sampling (Yap et al., 2014), Under-Sampling (Liu
et al., 2008), and Hybrid Methods (Gulati, 2020). In
this guide we recommend the use of over-sampling
and under-sampling.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Over-sampling (OS). Over-sampling raises the
weight of the minority class by replicating or creat-
ing new minority class samples. There are different
over-sampling methods; moreover, it is worth noting
that the over-sampling approach is generally applied
more frequently than other approaches.
Random Over Sampler: This method increases
the size of the dataset by the repetition of the orig-
inal samples. The point is that the random over-
sampler does not create new samples, and the va-
riety of samples does not change (Li et al., 2013).
Smote: This method is a statistical technique that
increases the number of minority samples in the
dataset by generating new instances. This algo-
rithm takes samples of the feature space for each
target class and its nearest neighbors, and then
creates new samples that combine features of the
target case with features of its neighbors. The new
instances are not copies of existing minority sam-
ples (Chawla et al., 2002).
Under-sampling (US). Under-sampling is one of the
most straightforward strategies to handle the imbal-
anced data problem. This method under-samples the
majority class to balance the class with the minority
class. The under-sampling method is applied when
the amount of collected data is sufficient. There
are different under-sampling models, such as Edited
Nearest Neighbors (ENN) (Guan et al., 2009), Ran-
dom Under-Sampler (RUS) (Batista et al., 2004) and
Tomek links (Elhassan and Aljurf, 2016), which are
the most popular.
3.3 Phase 3: Building Predictive Models
This phase aims to generate predictive models and
analyze their results. For this purpose we introduce
pipeline, pipeline is a Sklearn class (Pedregosa et al.,
2011) to sequentially apply a list of transformations
and final estimator on a dataset. Pipeline objects
chain multiple estimators into a single one. This is
useful since a machine learning workflow typically
involves a fixed sequence of processing steps (e.g.,
feature extraction, dimensionality reduction, learn-
ing and making predictions), many of which perform
some kind of learning. A sequence of N such steps
can be combined into a pipeline if the first N 1
steps are transformers; the last can be either a predic-
tor, a transformer or both (Buitinck et al., 2013). For
evaluating statistical performance in pipeline, we use
a GridSearch with K-Fold Cross Validation. Cross-
validation is a resampling procedure used to evaluate
machine learning models on a limited data sample.
K-fold Cross-Validation involves randomly dividing
the set of observations into k groups, or folds, of ap-
proximately equal size. The first fold is treated as a
validation set, and the method is fit on the remaining
k 1 folds. For example, using the mean squared er-
ror as score function, MSE
, is then computed on the
observations in the held-out fold. This procedure is
repeated k times; each time, a different group of ob-
servations is treated as a validation set. This process
results in k estimates of the test error, MSE
,..., MSE
. The K-Fold Cross-Validation estimate is
computed by averaging these values (James et al.,
2013). In our case, we focus on making predictions in
the data set for classification and regression tasks, our
models will have the algorithms chosen by the user
according to the task to be performed. In order to
compare the models’ performance, it is necessary to
use suitable metrics. After running the pipeline, just
take the metrics previously chosen by the user to be
calculated and presented to the user in an explana-
tory way about each selected metric. The last step
in this phase consists in ensuring the experiment’s re-
producibility in order to verify the credibility of the
proposed study. (Olorisade et al., 2017) have evalu-
ated studies in order to highlight the difficulty of re-
producing most of the works in state-of-art. Some au-
thors have proposed basic rules for reproducible com-
putational research, as (Sandve et al., 2013), based on
these rules we save all the decisions made by the user,
the results obtained whether they are tables or graphs,
the seed and settings of the algorithms used, all in a
final document. Figure 3 illustrates the activities that
make up the third phase of the proposed guide.
Figure 3: Phase 3 - Building Predictive Models.
4 DSAdvisor
DSAdvisor is an advisor for non-expert users or
novice data scientists, which following the stages
of the guideline proposed in this paper. DSAdvi-
sor aims to encourage non-expert users to build ma-
chine learning models to solve predictive tasks (re-
gression or classification), extracting knowledge from
their own data repositories. This tool was developed
in CSS3, HTML5, Flask (Grinberg, 2018), JavaScript
A Practical Guide to Support Predictive Tasks in Data Science
and Python (van Rossum, 1995).
DSAdvisor is an open source tool, developed us-
ing the Python programming language. So, this make
possible to reuse the large number of Python APIs
and Toolkis currently available. Besides, DSAdvi-
sor provides different resources to support data un-
derstanding and missing values detection. In addic-
tion, DSAdvisor guide the user on the task of out-
lier detection showing the number of instances, the
percentage of outliers found and the total of outliers.
If the user wishes to know precisely what these val-
ues are, they can go to the outliers table option to
check the position and value of the outliers for each
variable. Furthermore, the DSAdvisor tool help the
user on the feature selection task, running the follow-
ing filters methods: Chi Squared, Information Gain,
Mutual Info, F-Value and Gain Ratio. In order to
tackle the imbalanced data problem, DSAdvisor sup-
ports three alternatives: Oversampling, Undersam-
pling and Without resampling techniques. To assess
which type of problem is being addressed (classifica-
tion or regression), DSAdvisor asks some questions to
the user. Next, based on the user responses, DSAdvi-
sor suggests the most apropriate problem type. In case
of choosing classification, the pre-selected algorithms
will be logistic regression, naive bayes, support vec-
tor machine, decision tree, and multi layer perceptron.
In case of choosing regression, the pre-selected algo-
rithms will be linear regression, support vector ma-
chine, multi layer perceptron, radial basis function.
After this, DSAdvisor suggest the most suitable per-
formance metrics. Finally, the user has the option to
download the execution log of all the options selected
previously, the results obtained whether they are ta-
bles or graphs, and the settings of all used algorithms.
This option ensures that the experiments performed in
the DSAdvisor tool are reproducible by other users.
In this paper, we propose a guideline to support pre-
dictive tasks in data science. In addition, we present
a tool, called DSAdvisor, which following the stages
of the proposed guideline. DSAdvisor aims to en-
courage non-expert users to build machine learning
models to solve predictive tasks, extracting knowl-
edge from their own data repositories. More specif-
ically, DSAdvisor guides these professionals in pre-
dictive tasks involving regression and classification.
As future works we intent to carry out usability tests
and interviews with non-expert users, in order to eval-
uate DSAdvisor.
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A Practical Guide to Support Predictive Tasks in Data Science