Predict the accuracy of Linear Regression
There are several ways to check your Linear Regression model accuracy. Usually, you may use Root mean squared error. You may train several Linear Regression models, adding or removing features to your dataset, and see which one has the lowest RMSE - the best one in your case. Also try to normalize your data before fitting into Linear Regression model. 

The confusion matrix is used to check discrete results, but Linear Regression model returns predicted result as a continuous values. That is why you get the error: your dv_test data likely is integer, but y_pred is float.

You may try using classification model if it is suitable for the problem you try to solve - depends on what you try to predict. But for regression problem it would be better to use metric mentioned above.