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.   ention the names of any four stages of Design  hinking.
                Ans.  •  mpathi e
                     •  Define
                     •  Ideate
                     •   rototype
                     •   est.
                  .    plain the term  ime series decomposition.
                Ans.   ime series decomposition involves thinking of a series as a combination of level, trend.  easonality and noise
                     components.
                   Decomposition provides a useful abstract model for thinking about time series generally and for better understanding
                     problems during time series analysis and forecasting.
                  .   ive t o points of difference bet een  ross  alidation and  rain  est  plit.
                Ans.

                      CROSS VALIDATION                             TRAIN-TEST SPLIT

                       ross validation is preferable in cases  here your    he  rain  est procedure is appropriate  hen there is
                      dataset is relatively small.                 a sufficiently large dataset available.

                       ross validation gives you a more reliable    rain  est split does not gives a more reliable
                      measure  accuracy of your model s quality.   measure  accuracy of your model s quality.

                       plitting data into multiple sets.            plitting data into   sets    rain, test)

                  .   electing the right analytical approach depends on the questions being asked.
                   In the light of the given statement,  hich type of questions can be asked for
                   A.     lassification approach
                    .    Descriptive approach
                Ans.  A.     A  I I A IO  A   OA      uestions being asked,  hich require a yes no ans er.
                       .    D    I  I   A   OA      uestion being asked  hich sho s the relationship.

                  .   he follo ing is the diagram depicting the  oundational methodology for data science.


                                                   A                  Analytic approach


                                                                                         Data
                                  eedback
                                                                                      requirements


                                    D




                                                                                         Data
                                  valuation
                                                                                     understanding


                                                                           Data

                                                                         preparation

                Ans.  A      I       D    A DI

                                                                                       C apstone  P roj e ct
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