Gradient Descent Implementation

This project implements Gradient Descent from scratch to analyze how different hyperparameters—number of epochs and learning rate—affect the Mean Square Error (MSE).

🚀 Overview

Gradient Descent is an optimization algorithm used to minimize the loss function in machine learning models. This project:

• Implements Gradient Descent without external ML libraries.
• Compares different learning rates and epoch values.
• Evaluates the impact on Mean Square Error (MSE).

📌 Features

• Custom implementation of Gradient Descent.
• Adjustable hyperparameters: epochs and learning rate.
• Visual analysis of MSE convergence.