Demo notebook and data for Spark Summit Dublin 2017: One-Pass Data Science with Generative T-Digests

"A set of Jupyter Notebooks on feature selection methods in Python for machine learning. It covers techniques like constant feature removal, correlation analysis, information gain, chi-square testing, univariate selection, and feature importance, with datasets included for practical application.

SHAP is a fancy tool for interpreting feature importance in machine learning tasks. This Jupyter notebook gives a demonstration.

Set of Jupyter notebooks and geospatial data developed by the MAPSPADES project to study desertification in the Algerian steppe using EO data.

The repository contains comprehensive assessment reports and Jupyter Notebook files aimed at addressing key questions related to predicting wireless churn and identifying the features driving churn.

Jupyter notebook using machine learning techniques to explore the complex drivers of modern slavery. Models from a research paper are replicated and evaluated . Actions also include filling missing data, training regression models, and analyzing feature importance.