Here's my project guide on Towards Data Science based on this same project where I have gone in detail over how to make this complete end-to-end machine learning project, while explaining the mathematical foundations of the model.
- Implementation of a machine learning model in PyTorch that uses a polynomial regression algorithm to make predictions. The aim is to 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, the aim is to develop 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.
This is a predictive-modelling, regression problem where on the basis of a given set of features, the model has to predict the price of the car.
The dataset used in the project is an unprocessed one. This means that in order to be able to use it, it has to be preprocessed first. Also, out of the various set of features available, we need to eliminate some features while selecting the features that will be the best for training/testing the model.
After working on the project, here's what my key takeaways from it were.
- Data wrangling
- Data visualization
- Data analysis
- Feature selection
- Creating a machine learning from scratch
- Getting familiar with the PyTorch tensor operations