Implementation of Polynomial Regression

Introduction

In this project, you will learn how to implement polynomial regression using the method of least squares. Polynomial regression is a fundamental machine learning technique used to fit a polynomial function to a set of data points. This project will guide you through the process of loading and preprocessing the data, creating the Vandermonde matrix, and solving the polynomial regression problem using the least squares method.

🎯 Tasks

In this project, you will learn:

• How to load and preprocess the data for polynomial regression
• How to create the Vandermonde matrix, which is a key component for solving the polynomial regression problem
• How to use the method of least squares to solve for the optimal fitting coefficients of the polynomial regression

🏆 Achievements

After completing this project, you will be able to:

• Load and preprocess data for polynomial regression
• Create the Vandermonde matrix for polynomial regression
• Use the method of least squares to solve the polynomial regression problem
• Round the coefficients of the polynomial regression to a specified number of decimal places