Page 131 - Artificial Intellegence_v2.0_Class_12
P. 131

#  Sp lit the d ata into tr aining and  testing sets

                                                                            y
            X _ tr ain,  X _ test,  y _ tr ain,  y _ test =  tr ain_ test_ sp lit( X ,   ,  test_ siz e= 0 .2 ,  r and om_ state= 4 2 )
            # Create and fit the linear regression model
            mod el =  L inear Regr ession( )
            model.fit(X_train, y_train)

            #  M ak e p r ed ictions on the test set
            y _ p r ed  =  mod el.p r ed ict( X _ test)

            #  E v alu ate the mod el
            mse =  mean_ sq u ar ed _ er r or ( y _ test,  y _ p r ed )
            p r int( " M ean Sq u ar ed  E r r or : " ,  mse)

            #  P r ed ict cr op  y ield  w hen amou nt of  f er tiliz er  ( x )  is 2 5
            x _ new  =  np .ar r ay ( [ [ 2 5 ] ] )
            y _ new  =  mod el.p r ed ict( x _ new )
            p r int( " P r ed icted  cr op  y ield  f or  x  =  2 5 : " ,  y _ new )
            Output
             ean  quared  rror   .

             redicted crop yield for            .
             o check the e ecution of the above code, click on the follo ing link
            https   colab.research.google.com drive  p   m p  g m      d  o  m tqa    usp sharing

            C r eating  an A I Model using  L og istic R eg r ession in Python

            f r om p and as imp or t r ead _ csv
            f r om sk lear n.mod el_ selection imp or t tr ain_ test_ sp lit
            f r om sk lear n.linear _ mod el imp or t L ogisticRegr ession


            # load  d ataset
            d ata= " http s: //r aw .githu b u ser content.com/j b r ow nlee/D atasets/master /ir is.csv "
            lab els= [ ' sep al- length' , ' sep al- w id th' , ' p etal- length' , ' p etal- w id th' , ' class' ]
            d ataset= r ead _ csv ( d ata, names= lab els)
                  (
            p r int d ataset.shap e)
                  (
            p r int d ataset.head ( 2 0 ) )
            p r int d ataset.d escr ib e( ) )
                  (
            Output

            ( 1 5 0 ,  5 )

                sep al- length         sep al- w id th   p etal- length   p etal- w id th         class
              0                5 .1              3 .5              1 .4             0 .2    I r is- setosa

              1                4 .9              3 .0              1 .4             0 .2    I r is- setosa
              2                4 .7              3 .2              1 .3             0 .2    I r is- setosa

              3                4 .6              3 .1              1 .5             0 .2    I r is- setosa


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