By Pratham Barekal .

Code of this whole document - https://github.com/Quartz1605/RegressionsFromScratch/tree/main/LinearRegression

What is Multiple Linear Regression ??

• Multiple Linear Regression is a statistical technique used to model the relationship between one dependent variable (output) and two or more independent variables (inputs/features), assuming this relationship is linear.
• The assumption that the relationship is Linear is really important.

Mathematical Intution :

$$ Y=β0+β1X1+β2X2+⋯+βnXn+ϵ $$

where

Y → stands for output.

X1, X2, X3…. Xn → Predictor variables.

ϵ → Random Noise present already in the data.

β1,β2…βn → Coefficients that represent the impact of each feature on Y

β0 → Intercept

Difference between Multiple Linear Regression and Linear Regression :

Linear Regression uses only one input variable to predict an output, while Multiple Linear Regression incorporates several variables to make predictions, making it more suitable for real-world scenarios.