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SmartTab: Instant ML Predictions for Tabular Data is a Python-based ML project built on TabPFN for instant, high-accuracy predictions on tabular data. It requires no training or tuning, supports classification and regression, and is ideal for students and professionals working with structured datasets.

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SmartTab

PyPI version MIT License Open in Colab

SmartTab is a next-generation, transformer-based machine learning library for tabular data. It delivers instant, high-accuracy predictions for classification and regression, with zero manual tuning required. Built for professionals and researchers who demand speed, reliability, and ease of use.


About SmartTab

SmartTab leverages advanced transformer architectures—originally designed for natural language processing—to bring state-of-the-art performance to tabular datasets. Unlike traditional ML models, SmartTab can automatically handle heterogeneous data types, missing values, and complex feature interactions, all with minimal configuration. It is ideal for:

  • Automated machine learning (AutoML) pipelines
  • Rapid prototyping and research
  • Production systems requiring robust, fast tabular predictions
  • Users who want high accuracy without manual feature engineering or hyperparameter tuning

SmartTab is designed to be both easy to use and highly extensible, making it suitable for data scientists, ML engineers, and researchers alike.


Features

  • Lightning-fast, accurate tabular predictions
  • 🤖 Transformer-inspired deep learning architecture
  • 🛠️ Simple, production-ready Python API
  • 📊 Supports both classification and regression
  • 📦 MIT licensed, open source

Installation

pip install smarttab

Quick Start

from smarttab import SmartTabClassifier
clf = SmartTabClassifier()
clf.fit(X_train, y_train)
preds = clf.predict(X_test)

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© 2025 yuktheshwar. Released under the MIT License.

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SmartTab: Instant ML Predictions for Tabular Data is a Python-based ML project built on TabPFN for instant, high-accuracy predictions on tabular data. It requires no training or tuning, supports classification and regression, and is ideal for students and professionals working with structured datasets.

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