In this recipe, we will explore how to code Gradient Descent to solve a Linear Regression problem. In the previous recipe, we demonstrated how to code GD to find the minimum of a quadratic function.
This recipe demonstrates a more realistic optimization problem in which we optimize (minimize) the least square cost function to solve the linear regression problem in Scala on Apache Spark 2.0+. We will use real data and run our algorithm and compare the result to a tier-1 commercially available statistic software to demonstrate accuracy and speed.
