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

For extensive instructor led learning

Feature engineering package with sklearn like functionality

Machine Learning in R

A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

Leave One Feature Out Importance

EvalML is an AutoML library written in python.

Features selector based on the self selected-algorithm, loss function and validation method

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

Easy to use Python library of customized functions for cleaning and analyzing data.

Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.

Fast Best-Subset Selection Library

A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

Methods with examples for Feature Selection during Pre-processing in Machine Learning.