Since all features and driving factors are available, we should now focus on regression algorithms that estimate the continuous target variables from these predictive features.
The first thing we think of is linear regression. It explores the linear relationship between observations and targets and the relationship is represented in a linear equation or weighted sum function. Given a data sample x with n features x1, x2, ..., xn(x represents a feature vector and x = (x1, x2, ..., xn)), and weights (also called coefficients) of the linear regression model w (w represents a vector (w1, w2, ..., wn)), the target y is expressed as follows:
Or sometimes, the linear regression model comes with an intercept (also called bias
