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he follo ing code gives graphs noise, trend, seasonal, residual).
f r om p and as imp or t r ead _ csv
f r om matp lotlib imp or t p y p lot
f r om statsmod els.tsa.seasonal imp or t seasonal_ d ecomp ose
ser ies = r ead _ csv ( ' http s: //r aw .githu b u ser content.com/j b r ow nlee/D atasets/master /air line-
p assenger s.csv ' , head er = 0 , ind ex _ col= 0 )
r esu lt = seasonal_ d ecomp ose( ser ies, mod el= ' mu ltip licativ e' , p er iod = 1 )
r esu lt.p lot( )
p y p lot.show ( )
rend
.
easonal .
.
1
esid
hese graphs sho that there is no seasonality.
lick on the link to access the code
https colab.research.google.com drive p m p g m d o m tqa usp sharing.
Using an Analytical Approach
In data science, it is common to solve problems and ans er questions using data analysis. ypically, data scientists build
a model to predict outcomes or e plore underlying patterns for information gathering purposes. Organisations can then
use this information to take actions to ideally improve future outcomes. here are many rapidly evolving technologies
for data analysis and model building. In a remarkably short time, there have been significant improvements in the quality
and accuracy of the models too.
C apstone P roj e ct

