PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

An R implementation of TabNet

📊 A comprehensive comparison of TabNet and XGBoost across binary classification, multiclass classification, and regression tasks, showcasing performance metrics and fine-tuning results.

A collection of team projects

Classifying Travel Mode choice in the Netherlands using KNN, XGBoost, RF and TabNet

this project utilizes difference deep-learning algorithms to detect fraud operations on banking systems ( classification problem )

🧪categorical tabnet research part🧪

We will conduct a comprehensive analysis of the dataset, focusing on identifying key features that influence outcomes. To achieve this, we will employ Logistic Regression and TabNet models to discern feature importance.

Regression task using techniques of Machine Learning, Deep Learning and Transformers

Predicting writing quality based on data statistics of the writing process. The key lies in feature engineering and tree models.

Tabular Data Processing lightgbm, tabnet, resnet

Project developed for the Data Analytics course of the UniBO Master's Degree

The aim of this project is to experiment with various machine learning models that predict whether or not a patient will show up for a scheduled appointment. The project includes data processing and analysis. Also explainable AI methods are incorporated.

Modification of TabNet as suggested in the Medium article, "The Unreasonable Ineffectiveness of Deep Learning on Tabular Data"

image transformation and enhancement based attacks on fingerprint presentation attack detection systems

Kaggle Competition

Solar Flare Prediction Using Deep Learning

🏆신용카드 사용자 연체 예측 AI 경진대회 2등 솔루션🏆