This repository provides a step-by-step implementation and visualization of the cost function used in simple linear regression. It aims to help beginners understand how the cost function behaves with respect to model parameters (slope and intercept) and how it guides the optimization process.
🔍 Features:
Manual implementation of simple linear regression using Python
Calculation of Mean Squared Error (MSE) as the cost function
2D and 3D visualizations of the cost surface
Gradient descent intuition and cost landscape exploration