Hello,

After development of recommendation engine with the R, before removal of
outliers from data-set value of residual standard error was 1351 and after
removal of outlier its 100. Still there is no accurate prediction which
gives 10% correct(near) prediction. For more fitting i also have tried
polynomial model with two ,three and four degree but still no improvement.
Is there any most important thing to consider without R-squared or adjusted
R-squared.

Where i am using dataset with linear regression model for prediction of
product purchase revenue on the base of total numbers of time product added
to cart, removed from cart, total numbers of page views of product page.
For checking model prediction accuracy i am considering only minimum
residual standard error.

Thanks

Vignesh

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