Page 123 - Artificial Intellegence_v2.0_Class_12
P. 123

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
   118   119   120   121   122   123   124   125   126   127   128