I wrote a clear educational breakdown of Linear Regression starting from the basic idea, deriving the slope and intercept from the MSE loss function, and implementing the entire model from scratch in Python without using scikit-learn.
Summary of what it covers:
How MSE is formed from point-to-line errors
Why partial derivatives are used to minimize the loss
Derivation of:
b=ỹ-mx
m = E(x-X)(y-y) / E(x-x)²
Full Python implementation using NumPy
Visualization of the best-fit line
Comparison with sklearn’s LinearRegression
Full article link: Linear Regression From Scratch: Derivation, Intuition, and Complete Python Implementation [https://medium.com/@vk133162/linear-regression-from-scratch-derivation-intuition-and-complete-python-implementation-730569ccf003](https://medium.co…
I wrote a clear educational breakdown of Linear Regression starting from the basic idea, deriving the slope and intercept from the MSE loss function, and implementing the entire model from scratch in Python without using scikit-learn.
Summary of what it covers:
How MSE is formed from point-to-line errors
Why partial derivatives are used to minimize the loss
Derivation of:
b=ỹ-mx
m = E(x-X)(y-y) / E(x-x)²
Full Python implementation using NumPy
Visualization of the best-fit line
Comparison with sklearn’s LinearRegression
Full article link: Linear Regression From Scratch: Derivation, Intuition, and Complete Python Implementation https://medium.com/@vk133162/linear-regression-from-scratch-derivation-intuition-and-complete-python-implementation-730569ccf003