The article explains regression analysis, a method to understand relationships between variables. It covers linear regression basics, like R2 and F-statistic, and explores multidimensional and polynomial regression. It also discusses ways to prevent overfitting in models, such as lasso, elastic-net, and ridge regression. Other regression methods like support vector regression, decision trees, and k-Nearest-Neighbor Regression are also mentioned. The article delves into using deep learning and artificial neural networks for regression analysis.