Linear Regression Implementation
#Direct Method -Least Square cost function is used to find the parameter. -The direct method finds the inverse of the parameter W directly. -Data Input should have feature attributes(unknowns) to be less than the total no of sample points(Data3).
#Iterative Method -Least Square cost function is used to find the parameter. -The iterative method estimates the parameter W by applying optimization technique: gradient descent. -Data Input should have feature attributes(unknowns) to be less than the total no of sample points(Data3).
#Lagrange Method -Lagrange constrained optimization technique is used to estimate the unknown parameter. -Data Input should have feature attributes(unknowns) to be greater than the total no of sample points(Data4).
#Cross validation -Code written for performing hold out and k-fold cross validation operations prior to performing linear regression
#Gradient Descent & Stochastic Gradient Descent