Gaurav Gupta's Projects
Open source documentation of Microsoft Azure
This repository houses some of the links which I found useful for data science and machine learning.
Generate Diverse Counterfactual Explanations for any machine learning model.
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.
A Python package to assess and improve fairness of machine learning models.
Config files for my GitHub profile.
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
The Interpret Community extends Interpret repo with additional interpretability techniques and utility functions to handle real-world datasets and workflows.
This repository houses resources for debugging performance issues and improving performance of python programs.
Repository for resources on python programming and related frameworks
Often conda operations to create and manage conda operations may become challenging if you have too many conda virtual environments. This repo creates a assistant for you using which you can manage the conda related operations more easily.
Repository for useful material on Typescript programming language.