Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

Hierarchical Time Series Forecasting with a familiar API

Nyoka is a Python library that helps to export ML models into PMML (PMML 4.4.1 Standard).

Input Output Hidden Markov Model (IOHMM) in Python

A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.

Support financial data science workflow, manage large structured and unstructured data sets, and apply financial econometrics and machine learning

Exercícios do curso "Profissao: Cientista de Dados", sendo realizado pela EBAC - Escola Britânica de Artes Criativas e Tecnologia.

Python package for Scailable uploads

Learning Data Science

EDGAR Analytics – Python Library for Extracting, Analyzing, and Forecasting SEC EDGAR Filings. Streamline your financial analysis with comprehensive metrics, growth rates, and automated reporting capabilities.

output the results of multiple models with stars and export them as a excel/csv file.

Statstests: a Python package that provides a complement of process and statistical tests for statsmodels statistical models.

This repo contains various data science strategy and machine learning models to deal with structure as well as unstructured data. It contains module on feature-preprocessing, feature-engineering, machine-learning-models, bayesian-parameter-tuning, etc, built using libraries such as scikit-learn, keras, h2o, xgboost, lightgbm, catboost, etc.

Algo trading strategy, entrance task to CMF, Quantitative Analytics program, 2021

Supporting material for the Open Risk Academy course "Exploratory Data Analysis using Pandas, Seaborn and Statsmodels"

🧱 Wrappers for 3rd party models to be used with fold (https://github.com/dream-faster/fold)

Automating Assumption Checks for Regression Models (Work in Progress, Currently Paused)

Forecasted traffic volume from 2016-2018 data to reduce traffic congestion and efficiently schedule road maintenance using time series models.

🚀 Utility classes and functions for common data science libraries