Mambular is a Python package that simplifies tabular deep learning by providing a suite of models for regression, classification, and distributional regression tasks. It includes models such as Mambular, TabM, FT-Transformer, TabulaRNN, TabTransformer, and tabular ResNets.

Classification in TabularDataset

Multi-class attack detection on NSL-KDD dataset using TabTransformer

This project detects credit card fraud using a Kaggle dataset of over 1.8 million transactions. To handle the highly imbalanced data, it uses SMOTE to balance fraudulent and legitimate transactions. The project then trains several models, with the LightGBM classifier achieving the best performance at 99.5% accuracy.

This repository employs two different architectures of Tabular Transformer models for Operating System fingerprinting from three different datasets.

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

This project aims to enhance the detection and categorization of pancreatic cancer by applying advanced Python-based machine learning and deep learning methodologies. Utilizing popular algorithms remarkable accuracy was achieved in classifying pancreatic cancer through urinary biomarkers.

Implementation of TabTransformer: Tabular Data Modeling Using Contextual Embeddings in tinygrad.