Linear regression exercise and application in machine learning
Linear regression is a type of predictive analysis that concerns two-dimensional sample points with one independent variable and one dependent variable and determines a linear function that best fits the observed and, making it possible to predict the dependent variable values as a function of the independent variable.
Three models are created one using the Scikit-learn libraries, the OLS method, and Gradient Descent.
Ordinary least squares (OLS) is a statistical method of analysis that estimates the relationship between one or more independent variables and a dependent variable, in a linear regression model. It estimates the relationship by minimizing the sum of the squares in the difference between the observed and predicted values of the dependent variable configured as a straight line.
Gradient Descent is the process of minimizing a function by following the gradients of the cost function.