Recognition of Persomnality Types from Facebook status using Machine Learning

The analysis and classification of sleep, cardiovascular metrics, and lifestyle factors, for close to 400 fictive persons aiming to identify whether a potential client is likely to have a sleep disorder.

This repository serves as a platform to upload new code updates for my Master's Thesis (TFM), focused on the utilization of both supervised and unsupervised models on a dataset extracted from Spotify. It also includes a small fragment of my thesis. For more information, please contact me at:

Analysis of the Avila bible dataset from the UCI repository using several machine learning algorithms.

Sub-seasonal temperature and heatwave prediction in Central Europe with AI (linear and random forest machine learning models)

A Real Time Expression Detector using Python and Machine Learning

This project focuses on analysing the performance of various Machine Learning models available in python's scikit-learn package when trying to predict wine classification

Our project utilizes machine learning models to predict cardiovascular diseases (CVDs) by analyzing diverse datasets and exploring 14 different algorithms. The aim is to enable early detection, personalized interventions, and improved healthcare outcomes.

Fall 2020 - Computational Medicine - course project

Project for the ING Lion's Den Competition. The goal of this project is to predict defaults of Big Lion Bank’s customers and I build logit, ridge, KNN and XGBoost models.

We prepare a machine learning model that can be used to propose potential novel effective drugs to fight SARS-CoV-2, the virus responsible for COVID-19.