I am working on building a regression model to predict housing sales price using house features (Ames housing dataset). And I prepared feature set in two ways
I performed boxcox transformation on all the numerical features and performed one hot encoding on the categorical features.
I used the numerical feature set as it is and performed one hot encoding on the categorical features.
I applied linear regression on both feature sets and got r2 score much much lower in Case 1 (where numerical features were transformed). In Case 2 it was always more than 0.85 for any train test combination and in Case 1 it was never more than 0.5.
Since linear regression assumes the features to be conditionally independent and normal distributed and box cox helps to make features near normally distributed I thought performing box cox will boost my r2 score. Can anyone help me understand why did the r2 score plummeted for the model applied on box-cox transformed data?