o Some code cleanup.
o Documenting the linear regression methodology employed

- Select all data from the training set

- Remove all fields which have the same value for all observations (if
  we don't do this, we will encounter a singular matrix in our
calculations). This also makes sense because existence of a constant
field does not provide any extra information to our predictor.

- Use the linear regression model beta = (x_t * x)^-1 * x_t * y

- Remove the fields excluded in step 2 from the test samples as well

- Calculate a value for each test observation (x) defined by val = beta * x

- If val > 0.5, then it would be class 1 else class 0