Project for the Machine Learning course [ML654AA]

The course introduces the machine learning principles and models, including basic theory of learning.

An extensive experimental setup of ML models for multivariable regression

Abstract: This project tests the effectiveness of multiple Machine Learning models and libraries on a regression task, namely: Decision Tree, Extra Tree, K-Nearest Neighbors, Support Vector Machine, Random Forest, Bagging and MLP. At the end, we implemented an ensemble model with the top five performing models.

Project type: B

Developed by: Veronica Pistolesi, Francesca Poli (lady-de-bugs) 🐞
Academic year: 2022/2023
Master degree: Computer Science, Artificial Intelligence curriculum

This repository contains our final project for the exam, composed of two distinct assignments:

• MONK: classification task using the UCI's MONK problems dataset
• CUP: multivariable regression task using the CUP dataset given by course professor Alessio Micheli.