We have a Housing data set and we want to predict the price of the house. This python code helps us to illustrate Linear regression using the data set.
Techniques of Supervised Machine Learning algorithms include linear and logistic regression, multi-class classification, and many more. This kind of learning requires that the data used to train the algorithm is already labeled with correct answers. Supervised learning problems can be further grouped into Regression and Classification problems. The goal is to construct a model which can predict the value of dependent attribute from the attribute varibles. The difference between the two tasks is the fact that the dependent attribute is numerical for regression and categorical for classification.
Regression: A regression is when the output variable is a real or continuous value, such as 'salary' or 'weight'. Linear-regression simply helps us through this. It fits the data with the best hyper-plane which goes through the points.