Demonstrating how Gradient Descent algorithm can solve Linear Regression problems
Linear Regression helps to find a line of best fit, which goes close to the set of given points
Equation of straight line is,
y = mx + b
where m is the slope, b is the y-intercept for each point p(x, y)
Once we have line equation, we can find any "dependent variable" y with corresponding "explanatory variable" x
We can get Y-values for each X and it may not match with actual Y-value difference is then calculated using
error = sqrt(mean((actual - predicted)^2)
We have to minimise this error by changing slope m & y-intercept b for the next iteration to best fit the line
Obtained line of best fit helps us find any prediction (dependent variable) based on given scenario (explanatory variable) as long as it relates to given set of points
In order to get better results, we have to set learning rate to minimum and iterations to maximum. but this takes lot of time and resources
so the sweet spot shold be decided based on data-set
used to find dependent variable in a linear relation on providing an explanatory variable
download this repo & run python file using Python3 interpreter for test-run
all credits to Matt Nedrich