This blog explains regression as a technique to analyze cause-and-effect relationships in different domains. It covers various types of regression, including Linear, Logistic, and Polynomial Regression. Linear Regression is explored in detail, along with its assumptions. The blog also discusses RMSE and R-squared for model evaluation. It demonstrates the implementation of Linear Regression in Python manually and using Sklearn library, achieving an accuracy of 83%. Overall, it provides a concise overview of regression and its practical application.