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.