An intelligent system based on neural networks to detect the occupancy of an office room from sensors data. Including implementation, testing, exploration of behaviour and explanation of the system, also optimisation.

Emotion detection from small images using CNN

This project details the various steps that I took to build my own spam email classifier that is able to classify an email as spam or non-spam(ham) via a set of its features.

In this project, the model is save and reused for prediction. Also, it is being containerize with docker to be ready for deployment.

A demonstration of MLflow working end-to-end on sample data sets.

Working through all the exercises for An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.

Exploring Different types of Supervised Learning Classifications within the Scikit-Learn package.

Classification of an imbalanced dataset using SMOTE oversampling technique and ML Algorithms - KNN , XGBoost and Naive Bayes classifier

The goal of this project is to analyze data related to a marketing campaign and subsequently develop a machine learning model that can predict customers' response to the campaign. The overall benefit of this application is the efficient utilization of marketing budget.

Predict diabetes using machine learning models. Experiment with logistic regression, decision trees, and random forests to achieve accurate predictions based on health indicators. Complete lifecycle of ML project included.

Benchmarking bank data to enhance marketing strategies. Models: Decision Tree and Random Forest. Libraries: Pandas, Matplotlib, Seaborn, Scikit-Learn, Numpy. Findings: Customer patterns and seasonal behaviors.

A project designing and evaluating the fairness of a predictive model for arrest decisions using the North Carolina Policing Dataset. The study compares logistic regression, KNN, and fine-tuned KNN to ensure high accuracy and fairness in predictions based on gender, age, and race.

Udacity Model Evaluation Project

Builded a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Exercises from 'An Introduction to Statistical Learning with Applications in R' by James et al.

Implementation of KNN regression and classification models in Python

Group project for the Curricular Unit of "Elements of Artificial Intelligence and Data Science" @ FCUP, Porto with @tomazcomz