Code for "Designing Decision Support Systems Using Counterfactual Prediction Sets". ICML 2024.

A library for multi-class and multi-label text classification

A library for multi-class and multi-label classification

CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy

This graduation project aims to control a drone using EEG signals to classify left-hand, right-hand, and rest states. We utilized the g.tec Nautilus EEG headset with 8 electrodes and employed the g.Recorder software for data acquisition.

Successfully fine-tuned a pretrained DistilBERT transformer model that can classify social media text data into one of 4 cyberbullying labels i.e. ethnicity/race, gender/sexual, religion and not cyberbullying with a remarkable accuracy of 99%.

Implementing a neural network from scratch in Python. Building a feedforward neural network and implementing backpropagation for training. Building a working neural network that can be trained on a simple dataset for multi-class classification.

Classification of stars, galaxies, and quasars using spectral characteristics.

Students Engagement Detection Using Hybrid EfficientNetB7 Together With TCN, LSTM, and Bi-LSTM (DAiSEE and VRESEE datasets)

Fast and customizable framework for automatic ML model creation (AutoML)

This project's aim is to categorize ecommerce products from their images. MobileNetV2 model fine-tuned with 18K retail product images accross 9 categories. Project deployed with Flask and containerized via docker

This repository contains models that predict the obesity level of patients based on their eating/lifestyle habits and physical condition.

Projects - "Training a MLP Neural network in Python (Numpy, PyTorch)" / ...

R package for automation of machine learning, forecasting, model evaluation, and model interpretation

Multi-class classification of drug resistance in MTB clinical isolates

Spotify Classification Problem 2023

Uma rede neural para a classificação da qualidade do vinho. 🍷

The goal of this competition is to use various factors to predict obesity risk in individuals, which is related to cardiovascular disease. Good luck!

Multi-label classification using LLMs, with additional enhancements using quantization and LoRA (Low-Rank Adaptation). Get better performance on GPU.

Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.