DAT-430 Module 1 Journal
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DAT 430 Journal
Approaches to Preprocessing Data Assignment
In data analysis, preprocessing plays an important role in shaping the quality of data for a given
project. In this journal, we'll explore the purpose and relevance of various data preprocessing
approaches in the context of two distinct projects. Project One focuses on HR attrition analysis,
while Project Two involves data visualization and predictive modeling for organizational
initiatives.
The first data preprocessing approach we’ll discuss is aggregation. Aggregation is the process
“…where data is collected and presented in a summarized format for statistical analysis,” (Orbit,
2023). It is typically used to generate summary statistics or metrics that provide a holistic view
of the data. In the context of the projects, aggregation could be useful in Project One for creating
high-level HR attrition metrics that summarize the overall trends in employee turnover. It can
help identify patterns or common factors contributing to attrition.
Another data preprocessing approach, sampling, is “…used to select, manipulate and analyze a
representative subset of data points to identify patterns and trends in the larger data set being
examined,” (TechTarget, 2023). In Project One, sampling can be beneficial when dealing with a
large HR attrition dataset, allowing for quicker analysis and visualization of patterns. In Project
Two, sampling can help establish a baseline by selecting a random subset of the HR attrition data
for initial analysis.
Data preprocessing approach of dimensionality reduction is the process of reducing the number
of features or variables in a dataset while preserving as much relevant information as possible,
(Maduranga, 2020). It can be used to simplify the analysis and visualization of complex data. In
Project Two, dimensionality reduction may be helpful when selecting the most informative
features for predictive modeling to determine the likelihood of success of organizational
initiatives.
Feature subset selection approach involves “…identifying and selecting a subset of relevant
features from a given dataset. It aims to improve model performance, reduce overfitting, and
enhance interpretability,” (GeeksForGeeks.org, 2023). This approach can enhance the accuracy
and efficiency of models. For both projects, selecting the right features from HR attrition data is
crucial for meaningful analysis and prediction.
Feature creation approach consists of generating new features from existing ones to improve the
predictive power of models. In Project Two, creating new features from HR attrition data, such
as attrition rates over time or engagement scores, may enhance the predictive analysis by
providing additional insights.
Discretization is the process of converting continuous variables into categorical ones, while
binarization involves transforming variables into binary form (0s and 1s), (JavaTPoint.com,
2023). This approach can be useful in simplifying the analysis of certain types of data. In Project
One, discretization can be applied to create categories of employee satisfaction levels, which
may help identify the relationship between satisfaction and attrition.
Variable transformation is used when “…variable(s) does not fit a normal distribution then…
data transformation [is used] to fit the assumption of using a parametric statistical test,” (Imdad
Ullah, 2015). Or in other words, it encompasses techniques like normalization and
standardization, which scale and transform variables to ensure they have a similar impact on
models. This approach is important for both projects, as it helps ensure fair comparisons between
different features, improving the quality of analysis and predictions.
In the context of the projects, the most suitable approach may vary. However, for both Project
One and Project Two, feature subset selection is critical. Identifying and using the most relevant
features from the HR attrition data is essential for achieving the project objectives. By selecting
the right features, you can focus on the factors that have the most impact on attrition and the
likelihood of success of organizational initiatives. This approach streamlines the analysis and
ensures that the chosen features align with the project scope and objectives.
References:
OrbitAnalytics.com. (
October
29
, 20
23
).
Data Aggregation
.
OrbitAnalytics.com
https://www.orbitanalytics.com/data-aggregation/#:~:text=Data%20aggregation%20is
%20the%20process,vast%20amounts%20of%20raw%20data.
Yasar, K
. (
October
29
, 20
23
).
data sampling
.
TechTarget.com
https://www.techtarget.com/searchbusinessanalytics/definition/data-
sampling#:~:text=Data%20sampling%20is%20a%20statistical,larger%20data%20set
%20being%20examined.
Maduranga, U
. (Mar
ch
16, 2020).
Dimensionality Reduction in Data Mining
.
T
owards
D
ata
S
cience.com
https://towardsdatascience.com/dimensionality-reduction-in-data-mining-
f08c734b3001#:~:text=Dimensionality%20reduction%20is%20the%20process,in
%20many%20real%2Dworld%20applications.
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GeeksForGeeks.org
. (
October
29
, 20
23
).
Feature Subset Selection Process
.
GeeksForGeeks.org.
https://www.geeksforgeeks.org/feature-subset-selection-process/
JavaTPoint.com
. (
October
29
, 20
23
).
Discretization in data mining
.
JavaTPoint.com.
https://www.javatpoint.com/discretization-in-data-mining
Imdad Ullah, M
. (August 6, 2015).
Data Transformation (Variable Transformation)
.
Basic
Statistics and Data Analysis.
https://itfeature.com/miscellaneous-articles/data-
transformation-variable-transformation