For this particular tutorial, we will build a Closed-Form Multiple Linear Regression algorithm and use it to forecast AAVE's (WETH Pool) future projected Lifetime Repayments as a practical example. Towards the second half-end of the tutorial, we will convert the model to Cairo enabling us to make the entire MLR system as well as the forecasts fully provable & verifiable.
The Principal Component Analysis (PCA) method is an unsupervised learning algorithm that aims to reduce the dimensionality of a dataset consisting of a large number of interrelated variables, while at the same time preserving as much of the variation present in the original dataset as possible.
This tutorial will guide you through implementing your first fully verifiable linear regression model in Cairo using the Orion framework. It covers replicating a basic linear regression model from Python to Cairo utilizing the Orion library.
In this tutorial, you will be guided on how to train your model using Quantized Aware Training, how to convert your pre-trained model to Cairo 1, and how to perform inference with Orion.