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.
In this challenge, we will implement and apply polynomial regression to fit a set of training samples with a curve generated by a polynomial equation. The objective is to use the least squares method to obtain the optimal fitting coefficients of the polynomial regression. The challenge requires completing a function that takes a sample CSV file as input and returns the coefficients of the polynomial regression fitting.
Teacher
Labby
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