Ahmed T. Hammad's Projects
All-in-One Docker with Ubunutu Desktop, R(latest), Python(3.8), Spyder(latest), Redis(latest)
Python implementation of a Random Forest based on the Causal tree algorithm
Script from: "Back to the fields: COVID-19 impact on agricultural activitydetected with satellite data" by A. T. Hammad, G. Falchetta, I. B. M. Wirawanb.
A python package with tools to perform causal inference using observational data when the treatment of interest is continuous.
Useful container for projects that requires both R and Python (to be used with Docker Machine)
Default configuration for Le Wagon's students
Docker container to use Elman recurrent net to learn sequential and time-varying patterns
EpetitionViz: An integrated tool to understand e-petitions and support policy decision
FastAPI & PostgreSQL starter kit with Docker. Revamped backend template for modern needs.
Generalized Random Forests
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
[IJAIT 2021] MABWiser: Contextual Multi-Armed Bandits Library
pbox R package. Exploring multivariate spaces with Probability Boxes
syntCF is an R package that provides a set of tools to estimate the effect of a program or a policy using a robust time series synthetic counterfactual approach coupled with the double difference estimator within a Machine Learning framework.
Main code for the paper "Probabilistic prediction of remotely sensed crop health and its food security implications" written by Ahmed T. Hammad, and Giacomo Falchetta"
Main code for the paper "The long-run welfare impact of hydrological extremes in sub-Saharan Africa"