Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data
Mathematics,
Год журнала:
2025,
Номер
13(7), С. 1176 - 1176
Опубликована: Апрель 2, 2025
This
study
aims
to
compare
the
accuracy
performances
of
different
machine
learning
algorithms
(Logistic
Regression,
Decision
Tree,
Support
Vector
Machines
(SVMs),
Random
Forest,
Artificial
Neural
Network,
and
XGBoost)
using
World
Happiness
Index
data.
The
is
based
on
2024
Report
data
employs
indicators
such
as
Ladder
Score,
GDP
Per
Capita,
Social
Support,
Healthy
Life
Expectancy,
Freedom
Determine
Choices,
Generosity,
Perception
Corruption.
Initially,
K-Means
clustering
algorithm
applied
group
countries
into
four
main
clusters
representing
distinct
happiness
levels
their
socioeconomic
profiles.
Subsequently,
classification
are
used
predict
cluster
membership
scores
obtained
serve
an
indirect
measure
quality.
As
a
result
analysis,
Logistic
SVM,
Network
achieve
high
rates
86.2%,
whereas
XGBoost
exhibits
lowest
performance
at
79.3%.
Furthermore,
practical
implications
these
findings
significant,
they
provide
policymakers
with
actionable
insights
develop
targeted
strategies
for
enhancing
national
improving
well-being.
In
conclusion,
this
offers
valuable
information
more
effective
analysis
by
comparing
various
algorithms.
Язык: Английский
A new approach for predicting academic performance
Procedia Computer Science,
Год журнала:
2025,
Номер
257, С. 1257 - 1262
Опубликована: Янв. 1, 2025
Язык: Английский
Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Май 22, 2025
Power
transformer
plays
a
crucial
role
in
the
power
networks.
Most
of
malfunctions
was
due
to
failure
insulating
systems.
The
utilities
keen
on
contentious
operation
network,
so
early
detection
faults
avoiding
undesirable
outage
from
service.
paper
state
is
an
indication
health
and
aging
it
may
lead
transformer,
some
periodic
routine
test
must
be
performed
insulting
oil
get
information
about
condition.
value
degree
polymerization
(DP)
key
state.
Various
recommended
tests
such
as
dissolved
gases
(DGA),
breakdown
voltage
(BDV),
interfacial
tension
(IF),
acidity
(ACI),
moisture
content
(MC),
color
(OC),
dielectric
loss
(Tan
δ),
furans
concentration
specifically
(2-furfuraldehyde
(2-FAL))
were
carried
out
correlate
between
these
variables
DP
then
collected
data
used
supply
XGBoost/LightGBM
build
artificial
intelligence
model
predict
results
indicated
that
great
ability
proposed
with
high
accuracy.
Of
various
configurations
for
two
classification
models,
one
achieved
perfect
prediction
accuracy
(100%)
other
showed
values
1.0
down
0.955.
Язык: Английский