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A comprehensive toolkit and benchmark for tabular data learning, featuring 30+ deep methods, more than 10 classical methods, and 300 diverse tabular datasets.
[ICML 2023] The official implementation of the paper "TabDDPM: Modelling Tabular Data with Diffusion Models"
Reactive datatables for large or small datasets
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.
ML models + benchmark for tabular data classification and regression
csvreader library / gem - read tabular data in the comma-separated values (csv) format the right way (uses best practices out-of-the-box with zero-configuration)
Useful code for tabular modeling and automation.
R interface to fast.ai
TAT-QA (Tabular And Textual dataset for Question Answering) contains 16,552 questions associated with 2,757 hybrid contexts from real-world financial reports.
Connect to PBI/AAS Models via Python
Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning
Official PyTorch implementation of STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables (ICLR 2023 Spotlight).
OLAP PivotTable Extensions is an Excel add-in which extends the functionality of PivotTables on Analysis Services cubes, the Excel Data Model (Power Pivot), and Power BI
Awesome Tabular Deep Learning for "Representation Learning for Tabular Data: A Comprehensive Survey"
A high-performance and low-allocating framework to work with tabular data
Easily get custom go template based outputs to your command-line tool. Like in docker/kubernetes