In the real world, outcomes are rarely determined by a single factor. Multiple Linear Regression allows us to consider dozens of variables simultaneously to produce highly accurate predictions.
1The Multivariate Equation
The simple line formula expands into a multi-dimensional plane: **y = b0 + b1*x1 + b2*x2 + ...**. Each 'x' represents a different feature (like Age, GPA, or Salary), and each 'b' is the weight the model assigns to that specific feature based on its impact on the final result.
2Handling Text Data
Machine learning models only understand math. When your data contains categories like 'City' or 'Department', you must use One-Hot Encoding. This process creates 'Dummy Variables'—binary columns (0 or 1) that represent the presence of a category without assigning a false numerical order to them.
3The Dummy Variable Trap
Including all dummy columns creates Multicollinearity, where variables can predict each other. This 'Trap' can confuse the model. The solution is to always drop one dummy column (N-1). For example, if you have two cities, one column is enough: if it's not City A (0), it must be City B (1).
