When it comes to predictive analysis, regression models prove to be one of the most cost-efficient methods. While a linear regression model can provide some good predictions, in several cases the polynomial regression models can vastly outperform simple linear models. In the following project, we’ll have a look at how to create a polynomial regression model in PyTorch from scratch.
Project Objectives
Implementation of a machine learning model in PyTorch that uses a polynomial regression algorithm to make predictions.
We will create the model entirely from scratch, using basic PyTorch tensor operations.
Using the model to conduct predictive analysis of automobile prices. At the end of the project, we aim at developing a highly efficient ML model that can predict the price of a car on the basis of its features.
Performing visual & descriptive analysis of the data to predict which features play a key role in determining the price of a car.

#machine-learning #pytorch #data-science #regression #data-visualization