It is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.
I am using boston housing data sets, which is provided by scikit-learn
The linear regression algorithm is one of the fundamental supervised machine-learning algorithms
Formula: y = a*x + b.
y is the dependent variable (that's the variable that goes on the y axis)
x is the independent variable (i.e. it is plotted on the x axis)
b is the slope of the line
a is the y-intercept
Out put of boston-housing.py:
Manually Loss-Function (a = 15.; b = -70.)
Lets use OLS for estimating the unknown parameters a and b
Out put of ordinary-least-square.py:
Estimated Loss Function
Another algorithm for estimating the unknown parameters a and b
Out put of stochastic-gradient-descent.py:
Estimated Loss Function via Stochastic Gradient Descent
