I am developing a linear model with 13 variables, including the target variable (online purchase revenue for items). So, I first built model1 with regular variable and then build model2 after normalizing of the data. I have copied the coefficients for two models here :
Model1(Without Normalized Data) Coefficients: (Intercept) xid xcartadd 6.386e+01 -4.301e-03 -1.229e+02 xcartuniqadd xcartaddtotalrs xcartremove 1.239e+02 7.788e-02 -1.424e+02 xcardtremovetotal xcardtremovetotalrs xproductviews 5.588e+02 -3.445e-02 1.369e+01 xuniqprodview xprodviewinrs xsizeselecteduniview -1.530e+01 5.401e-04 -1.299e+02 xsizeselectedtotalviews xsizeselectedtotalviewsrs 6.280e+01 -2.453e-02 Model 2(With Normalized data) Coefficients: (Intercept) xid 3.900e+02 -4.301e-03 xcartadd_n xcartuniqadd_n -2.623e+03 2.069e+03 xcartaddtotalrs_n xcartremove_n 1.785e+03 -1.721e+02 xcardtremovetotal_n xcardtremovetotalrs_n 4.474e+02 -5.360e+01 xproductviews_n xuniqprodview_n 7.979e+03 -7.378e+03 xprodviewinrs_n xsizeselecteduniview_n 4.757e+02 -1.218e+03 xsizeselectedtotalviews_n xsizeselectedtotalviewsrs_n 1.044e+03 -5.374e+02
and my questions are :
Is it appropriate to take only normalized data into model or non normalized data?
Is it appropriate to take combination of normalized data as well as non normalized data?
How can I choose most appropriate predictor variable from them for model?