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cleaning techniques, organizations can enhance the quality and
reliability of the data, reducing the risk of erroneous or misleading
analysis results.
iv. Data Analytics
In this phase, the data that have been processed can be use to identify
trends and patterns using statistical and computational methods. This
may entail applying machine learning algorithms to spot trends and
predict future events (Rawat, 2021). Through the analysis, researcher
able to identify trends, pattern and uncover any problems and make
data-driven decision.
e. Data Visualization
In this phase, the data analysis results are presented in a form that is
simple to comprehend and analyse. To communicate the conclusions
drawn from the data analysis, this may need the use of diagrams,
graphs, and other visualisations. Example of visualization is bar chart,
histogram, pie chart, heatmaps and plot graphs.
2.4 Data Visualization Techniques
According to Kaur and Kaur (2020), data visualisation is the representation
of data in a graphical or pictorial format for easy comprehension, and it is an
essential instrument for analysing complex datasets and identifying patterns
and trends. Using data visualisation techniques, the relationship between
datasets is illustrated.
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