This is why you need to segregate some of your data for validation purposes.
Split your data into two parts, perhaps selected randomly. For the model to your training data. What was the error in the fit? Note that.
Then test the model on the validation data. If the validation set has an error of roughly the same size as the training set, then you are not overfitting.
However, if the prediction error on the validation set is significantly larger than the training set, then you are overfitting your data.
