Hi,

I have a data set to predict the sales price for houses. I ran a PCA analysis on the continuous variable without changing or cleaning the data. The analysis suggested that 26 component out of the 31 componnet explained 96% of variability.

So I saved the values of these 26 component. Now I am trying to run a neural netwrok analysis by choosing all the 26 saved component and all the remaining categorical variables as** X factor and Sales price as Y**. What I realize is that when I increease the NTanH value in the first layer the R-Square keeps increasing. The R-Square increases for NTanH 3 to 10 then reduces at NTanH 15 and then again increases at NTanH 40 etc.

**WHY DID THE VALUE DECREASE FOR 15 AND THEN AGAIN START INCREASING??**

P.S ignore the the last three predicted sales price column.