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Use unsupervised and supervised learning to predict stocks
Ferramentas pedagógicas para a disciplina de Ambiente Macroeconomico Puc-PR
A collection of important graph embedding, classification and representation learning papers with implementations.
An R package that returns tidy data from the APIs of the Brazilian Federal Senate and Chamber of Deputies.
Codes for replication and implementation of techniques in our credit risk article
crime
Data Science Using Python
The Leek group guide to data sharing
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
📖An interactive companion to the well-received textbook 'Introduction to Econometrics' by Stock & Watson (2015)
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Visualizations for machine learning datasets
Shiny App for Capital Structure in IBOVESPA
Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians)
Easy access to shapefiles of the Brazilian Institute of Geography and Statistics (IBGE)
GluonTS - Probabilistic Time Series Modeling in Python
Generalized Random Forests
Hexagonal hierarchical geospatial indexing system
Fit interpretable models. Explain blackbox machine learning.
Practical techniques for training interpretable ML models, explaining ML models, and debugging ML models.
Material Para Introdução a Python
Notebooks and code for the book "Introduction to Machine Learning with Python"
Amazon Deep Learning's Keras with Apache MXNet support
Linear models including instrumental variable estimators and panel data models
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.