Implementation of the gradient descent algorithm for linear regression problem

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# Linear Regression with Gradient Descent in Python

### Overview

This repository contains an implementation of gradient descent for linear regression using Python. The purpose of this project is to demonstrate how gradient descent can be used to optimize a simple linear regression model.

### Dependencies

This project requires the following Python libraries:

``````NumPy
Matplotlib
``````

You can install the required libraries using the following command:

``````pip install numpy
pip install matplotlib
``````

### Usage

The notebook file contains the implementation of the gradient descent algorithm for a simple linear regression problem. You can modify the code to experiment with different datasets or tweak the parameters of the algorithm.

# Tutorial:

### 1. Introduction

Simple linear regression is a fundamental machine learning technique used to predict the relationship between two continuous variables. It aims to find the best-fit line that minimizes the error between predicted values and actual values.

Gradient descent is an optimization algorithm that helps find the optimal values for the model parameters by minimizing the cost function. In this tutorial, we will implement gradient descent for a simple linear regression problem using Python.

### 2. Prerequisites

• Basic understanding of Python programming
• Familiarity with NumPy and Matplotlib libraries

### 3. Dataset

We will use a small synthetic dataset (x = [1, 2, 3, 4, 5], y = [3, 4, 5, 6, 7]) to demonstrate the implementation of gradient descent for simple linear regression. Feel free to replace it with your dataset.

Implementation of the gradient descent algorithm for linear regression problem