Machine Learning - Supervised Learning - Multiple Linear Regression Tutorial

Multiple input column  & 1 output column

Multiple Linear Regression is a regression algorithms which models the linear relationship between a single dependent continuous variable and more than one independent variable.

Linear relationship is in the form of hyperplane in dimension more than 2. 

In geometry, a hyperplane is a subspace whose dimension is one less than that of its ambient space. For example, if a space is 3-dimensional then its hyperplanes are the 2-dimensional planes, while if the space is 2-dimensional, its hyperplanes are the 1-dimensional lines.

3d - y = B₀ + B₁X_{1 +} B₂X₂

4d - y = B₀ + B₁X_{1 +} B₂X_{2 +} B₃X₃

nd – y - B₀ + B₁X_{1 +} B₂X_{2 +} B₃X_{3 …+} B_nX_n

Y = B₀ + summation_{i=1 to n}(B_iX_i)