Here, I have tried to implement linear regression using Normal Equation, ie. using matrix forms.

theta = inv(X'X).(X'Y) , where...

• theta is coefficient vector
• Y is dependent variable vector
• X is matrix containing independent variables, with an additional row of 1's for intercept

1. no need to choose alpha -> learning rate
2. no need to iterate
3. no need to normalize the data
4. slow if n is too large (~10000)

• Redundant features: some are linearly dependent on each other
• Too many features: (m < n)
  1. delete some features
  2. use regularization