A comprehensive toolkit and benchmark for tabular data learning, featuring 35+ deep methods, more than 10 classical methods, and 300 diverse tabular datasets.

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ML models + benchmark for tabular data classification and regression

Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

A Living Benchmark for Machine Learning on Tabular Data

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Benchmarking different approaches for categorical encoding for tabular data

TabBench is a benchmark built to evaluate machine learning models on tabular data, focusing on real-world industry use cases.

(ICLR 2025 Spotlight) TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks

A minimal benchmark of various tools (statistical software, databases etc.) for working with tabular data of moderately large sizes (interactive data analysis).

A benchmark for distribution shift in tabular data

Benchmark tabular Deep Learning models against each other and other non-DL techniques

The TABLET benchmark for evaluating instruction learning with LLMs for tabular prediction.

Autocurator is a comprehensive benchmarking toolkit for evaluating synthetic tabular data. It measures fidelity, coverage, privacy, and utility through quantitative metrics, visual reports, and PCA/correlation diagnostics. Ideal for validating VAE, GAN, Copula, or Diffusion-generated datasets.

TabularBench: Adversarial robustness benchmark for tabular data

This repository contains the official implementation of "A Benchmarking Study of Kolmogorov-Arnold Networks on Tabular Data" (under review). You can use this codebase to replicate our experiments about benchmarking KAN networks on some of the most used real-world tabular datasets.

A benchmark of meaningful graph datasets with tabular node features

Data and code of the Findings of EMNLP'23 paper MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields

Energy Consumption-Aware Tabular Benchmark For Neural Architecture Search

TabMini: A Benchmark Suite for Evaluating and Analyzing the Data Efficiency of Tabular Classifiers

[NeurIPS 2025 D&B Track] Benchmarking Language Models on Imperfect Tabular Data

The comprehensive MMTU Benchmark for testing the MLLMs' capabillity in Tabular Understanding

Unlimited Data-Science Benchmarks for Numeric, Tabular and Graph Workloads

Dataset benchmark for tabular data with textual features

This is a personal project which aims to expand the current literature of benchmarking algorithm's performance on tabular datasets.

[Python3] The simulator for multi-fidelity or parallel optimization using tabular or surrogate benchmarks

Script and tabular results of TPC-DS benchmarks

The benchmark code to the paper "How well do LLMs reason over tabular data, really?"

Code for the paper "Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data".

A benchmark dataset for LLM-based generation of tabular data