Center points of world countries - CSV and GeoJSON

Centroid-UNet is deep neural network model to detect centroids from satellite images.

Code for the paper "DUCK: Distance-based Unlearning via Centroid Kinematics"

We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

object detections on polygonal roi using yolo

The Similarity Search Tree is an efficient method for indexing high dimensional feature vectors. The main objective of this data structure is to obtain the nearest neighbors given a certain query vector in a reasonable amount of time. In this project, the k-NN algorithm was adapted for supporting image retrieval.

Most of the problems I solved and algorithms I grinded while prepairing for the Russian Olympiad in Informatics.

Parallellization of the Kmeans algorithm with OpenMP

With this Python code it's possible to find the centroid of a regular or irregular geometric figures wich are solid or have holes, using Open CV library

Simple object tracking by using the centroid tracking algorithm

This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the objective here is to make a clear comparison between the sequential and parallel execution of the clustering steps.

Creates a new feature class with the centroid of all polygons for each category provided by a field.

Country centroids based on data from geoBoundaries.org. Useful for bubble maps and arc maps.

Data Analysis, EDA and Unsupervised Machine Learning Models on Uber NY Dataset

Análisis de datos (aprendizaje no supervisado), clasificación de los jugadores del juego FC25 en 4 clusters, aplicando 3 medidas diferentes para valorar la similitud.

This project focuses on developing a sentiment classification model using Multi-Layer Perceptrons (MLPs) with variations in text representation techniques and hyperparameter tuning, leveraging a balanced subset of the Kaggle Twitter Sentiment Analysis dataset. Additionally, a single instance of logistic regression was applied for comparison.

Using Supervised Machine Learning algorithms to identify credit risks

Analyse d'un groupement de pays cible pour l'exportation de poulet (clustering, CAH, k-means, ACP)

This project aims to build a complete pattern recognition system to solve classification problems using the k-Nearest Neighbors (KNN) algorithm. To classify chest X-ray images into three categories: COVID-19 positive, pneumonia positive, and normal. To achieve this, we utilize the COVID-19 Chest X-ray dataset available on Kaggle.

Classical Computer Vision