Serial Normal Equation Solver for linear regression using Gauss Elimination and Gauss Sidel methods

Housing data containing size in sq. ft., age, number of bedrooms, number of floors as attributes.

main.cpp - Contains the main() function

transpose.cpp - Function to calculate matrix transpose

multiply.cpp - Calculates product of two matrices/matrix and vector

gaussElimination.cpp - Direct solver code

gaussSiedel.cpp - Iterative solver

A.txt - File containing the data set (independant variable values)

B.txt - File containing dependant variable values

Depending on the number of features f, we get a f-dimensional "theta" vector as the output. The linear regression model has hence been built and can be used to make predictions.