Stars
A light-weight, flexible, and expressive statistical data testing library
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
Examples of python neural net and ML stock prediction methods with sample stock data.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Data imputations library to preprocess datasets with missing data
Exchange calendars to use with pandas for trading applications
A python library for user-friendly forecasting and anomaly detection on time series.
Anomaly detection related books, papers, videos, and toolboxes
TODS: An Automated Time-series Outlier Detection System
Diverse collection of 100 Hydrogen Torch Use-Cases by different industries, data-types, and problem types
A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.
Python training for business analysts and traders
This project provides implementations with Keras/Tensorflow of some deep learning algorithms for Multivariate Time Series Forecasting: Transformers, Recurrent neural networks (LSTM and GRU), Convol…
Scalable machine 🤖 learning for time series forecasting.
Calculates various features from time series data. Python implementation of the R package tsfeatures.
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Scalable and user friendly neural 🧠 forecasting algorithms.
An extension of XGBoost to probabilistic modelling
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
Arguably the best missing values imputation method.
Missing data visualization module for Python.
An open-source, low-code machine learning library in Python
tqchen / xgboost
Forked from dmlc/xgboosthttps://github.com/dmlc/xgboost