Feature-engineering Libraries

21 Components & Libraries

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Feature-engineering Libraries

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

To install the current release: Participating discussions via the following IM groups is also welcomed. Over the past few years, NNI has received thousands of feedbacks on GitHub issues, and pull req…

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get…

An open source python library for automated feature engineering

Install with pip You can install add-ons individually or all at once by running Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we…

SGX-Full-OrderBook-Tick-Data-Trading-Strategy

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

Framework to capture the dynamics of high-frequency limit order books. In this project I used machine learning methods to capture the high-frequency limit order book dynamics and simple trading strat…

[UNMAINTAINED] Automated machine learning for analytics & production

Automated machine learning for production and analytics auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on sing…

Your single tool to express data, ML, and LLM pipelines with simple python functions. Runs anywhere that python runs, E.G. spark, airflow, jupyter, fastapi, etc. Incrementally adoptable. Use Hamilton to build testable, reusable, and self-documenting dataflows with lineage and metadata out of the box.

Hamilton is a novel paradigm for specifying a flow of delayed execution in python. It works on python objects of any type and dataflows of any complexity. Core to the design of Hamilton is a clear ma…

A scalable general purpose micro-framework for defining dataflows. You can use it to build dataframes, numpy matrices, python objects, ML models, etc. Embed Hamilton anywhere python runs, e.g. spark, airflow, jupyter, fastapi, python scripts, etc.

The general purpose micro-framework for creating dataflows from python functions! Specifically, Hamilton defines a novel paradigm, that allows you to specify a flow of (delayed) execution, that forms…

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

Automatically save and learn from Experiment results, leading to long-term, persistent optimization that remembers all your tests. HyperparameterHunter provides a wrapper for machine learning algorit…

Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.

Temporian is an open-source Python library for preprocessing ⚡ and feature engineering 🛠 temporal data 📈 for machine learning applications 🤖

Temporian is currently available for Linux and MacOS (ARM and Intel). Windows support is under development. Our goal is to compute the sum of revenue for each store at 11 pm every weekday (excluding …

A collection of demos showcasing automated feature engineering and machine learning in diverse use cases

Building an accurate machine learning model requires several important steps. One of the most complex and time consuming is extracting information through features. Finding the right features is a cr…

We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.

Thus, you may use this library to improve your dataset quality: To run experiment follow these steps: To determine the best sampling strategy, ROC AUC scores of each dataset were scaled (min-max scal…

A query / expression language for performing computations on JSON-like structures. Tuned for clientside ML feature extraction.

MistQL is a query language for JSON-like structures, built for embedding across multiple domains. It supports logic for querying and manipulating JSON-like data in a simple, readable manner. MistQL s…

A machine learning preprocessing library over batch data, providing performant and Pandas-style easy-to-use API for model development

First, set up an environment. If you are using conda, create a conda environment: Experimental nightly binary on MacOS (requires macOS SDK >= 10.15) and Linux (requires glibc >= 2.17) for Pytho…

Code repository for the online course Feature Engineering for Machine Learning

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

TPOT stands for Tree-based Pipeline Optimization Tool. TPOT2 is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. Consider TPOT2 your Data …

Using data analytics and machine learning to create a comprehensive and profitable system for predicting the outcomes of NBA games.

Under the 'comprehensive' aspect, the project will explore and test all plausible prediction methods, ensuring an evolving scope and prioritization based on the utility of the results. This creates a…

This repository contains resources in the form of ebooks, which are related to Data Science, Machine Learning, and similar topics.

Regular practice on Data Science, Machien Learning, Deep Learning, Solving ML Project problem, Analytical Issue. Regular boost up my knowledge. The goal is to help learner with learning resource on Data Science filed.

Machine Learning Algorithm Data Cleaning Process Data Mining Tehnique Data Analysis Database Connection--- MySql, PostgresSql, Elasticsearch Dashboard Development Feature Engineering & Feature Se…

A toolkit to boost the productivity of machine learning engineers.

From PyPi: From GitHub: All heavy-duty computations are run on our serverless infrastructure and require an API key. To configure the package with your API key, run To start a Jupyter Notebook server…

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