Reducing MAE or RMSE of linear regression I am trying to guess a home price and at the end I intend to figure out a formula by using linear regression. As you can see here, I have 1480 samples with 45 features in which price(fiyat) is the target variable.
Do higher values for $RMSE$ and $MAE$ mean that the dataset cannot be trained in a good manner?
Is there a way to reduce these values?
As you can see $R2$ seems well.

How can we say that how much percentage of error occurs for the guesses on average? If we can how much could you say?
There is really substantial difference between the prices and guesses as being seen below:
You can try things like outlier removal, reducing data skewness, or stacking different models. For an advanced regression guide, checkout this kernel from Kaggle.