categorizing news: fake or not

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD).

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.

This repository contains code and data for analyzing real estate trends, predicting house prices, estimating time on the market, and building an interactive dashboard for visualization. It is structured to cater to data scientists, real estate analysts, and developers looking to understand property market dynamics.

Multimodal Sentiment Analysis using Text and Image Data on twitter dataset

Uses letter frequency and catboost classifier model in synchronous for guessing letters in hangman game instance. The model performance is evaluated on both seen words in the dictionary and words out of the dictionary.

ML-bot that detects toxicity in russian texts.

Comitê de Classificadores | Projeto N1

Classifying if a landslide occured or not

Командный проект по Векторной электрокардиографии

End-to-end cancer diagnosis using Machine learning and Flask for a web interface.

A Domestic violence support system for the victims, that enables users to share their thought and provides knowledge about the particular type of abuse they are going through.

A model on the streamlit framework predicts disease and makes a treatment recommendation

Android malware detection using machine learning.

Identify health insurance customers with interest in a vehicle insurance.

Tarefa da Aula 8

This is my final solution to the Mars-spectrometry challenge by NASA hosted on @drivendataorg

Machine Learning aplicado al mantenimiento predictivo. Se realizaron 2 modelos: 1 por medio de clasificación binaria que predice si una máquina fresadora estará en riesgo de fallar o no, y el 2 modelo a través de clasificación multiclase que predecirá el modo de falla

Discover a comprehensive approach to constructing credit risk models. We employ various machine learning algorithms like LightGBM and CatBoost, alongside ensemble techniques for robust predictions. Our pipeline emphasizes data integrity, feature relevance, and model stability, crucial elements in credit risk assessment.

REactive Behavior Constraint-Aware Tree learning (REBCAT) - a human-robot collaboration framework to learn task from demonstrations. Interpretable, fast, object-centric, and reactive.

Web Server Log Analysis

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contains files, notebooks and source raw data used to develop and train the models described in the article 10.1016/j.snb.2024.136116

This project focuses on predicting house prices using machine learning techniques. The dataset consists of over 1,000,000+ rows and 12 columns containing information about various house attributes. The goal is to build predictive models to estimate house prices based on these attributes.

Expresso Churn Prediction Challenge - dealing with imbalanced dataset

This notebook implements a structured machine learning pipeline to classify cosmic data using the CatBoost Classifier, known for its efficiency with categorical features and minimal preprocessing requirements.

Disaster Tweets Classifications by Machine Learning, which is a currently Kaggle Competition.

Machine learning project to predict obesity risk levels based on lifestyle and demographic data. This project utilizes advanced algorithms like CatBoost, LightGBM, and more to classify individuals into different obesity categories

Repository for the thesis study "Evaluation and Comparison of Boosted ML Models in Behavior-Based Malware Detection".

A real-time hand gesture recognition system using MediaPipe, OpenCV and CatBoost, trained on the Hagrid Gesture Dataset to classify 18 hand gestures with high accuracy."