Tips to improve Linear Regression model I have just run a Linear regression model on the Dataset having 7 independent variable and 1 target variable. Below is the R squared and MSE values.


Mean squared error for training set : 36530921.0123
$R^2$ value for training set : 0.7477


Can anybody please give me some tips to increase the efficiency of this model.

Edit: I have just implemented the same problem using Linear regression with Normalization of the features. 
I got the below output:
Mean squared error for training set : 5.468490570335696e-10
R2 value for training set : 0.9275088299658416
Mean squared error for training set : 4.111793316375822e-10
R2 value for training set : 0.9342888671422529

So can we consider normalizing the dataset to get better accuracy ?
You can build more complex models to try to capture the remaining variance. Here are several options:


Add interaction terms to model how two or more
independent variables together impact the target variable
Add polynomial terms to model the nonlinear relationship between an independent variable and the target variable
Add spines to approximate piecewise linear models
Fit isotonic regression to remove any assumption of the target function form
Fit non-parametric models, such as MARS