Multiple Linear Regression Explained Simply (Part 1)

Multiple linear regression extends simple linear regression by using two or more independent variables to predict a target variable. Instead of fitting a line, this method fits a plane to the data points in a multi-dimensional space. The optimal plane is the one that minimizes the sum of squared residuals, which are the differences between the actual and predicted values. To find the coefficients (slopes) and intercept that define this plane, calculus, specifically differentiation, is used to find the minimum of the error function. The process is illustrated with a sample dataset and Python code using scikit-learn.