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This page contains pilot studies conducted under the HLG-MOS Machine Learning Project and programming codes (if available). If you want your study or code to be added, please contact UNECE
You can search by Theme, ML method, Programme code availability and Programming Language using filter below.

Theme	Title	Country/Organisation	Data Source	ML methods	Programme code availability	Programming Language	Note
Coding & Classification	Occupation and Economic activity coding using natural language processing	Mexico	Survey data	Extra tree, Naive bayes, XGBoost, Support vector machine, Multilayer perceptron, Decision tree, Random forest, K-nearest neighbors, Logistic regression, Ensemble	Yes (Click File attachment)	Python
Coding & Classification	Industry and Occupation Coding	Canada	Survey data	FastText	Yes (Click GitHub link)	Python
Coding & Classification	Sentiment Analysis of twitter data	Belgium Flanders	Social media data	Word embedding, Logistic regression, XGBoost, Random forest	Yes (Click GitHub link)	Python
Coding & Classification	Coding textually described data on economic activity collected from Labour Force Survey	Serbia	Survey data	Random forest, Support vector machine, Logistic regression
Coding & Classification	Coding Workplace Injury and Illness	USA	Survey data	Neural network	Yes (Click GitHub link)	Python
Coding & Classification	Production description to ECOICOP	Poland	Web scraping data	Naive bayes, Logistic regression, Random forest, Support vector machine, Neural network	Yes (Click Github link)	Python
Coding & Classification	Automated Coding using the IMF’s Catalog of Time Series	IMF
Coding & Classification	Automatic coding of occupation and industry in social statistical surveys	Iceland	Survey data	Deep learning	Yes (See section 5 of the report)	R
Coding & Classification	Standard Industrial Code Classification by Using Machine Learning	Norway	Administrative data	Logistic regression, Random forest, Naive bayes, Support vector machine, FastText, Neural network		Python
Edit & Imputation	Imputation of the variable “Attained Level of Education” in Base Register of Individuals	Italy	Administrative data, Survey data, Census data	Multilayer perceptron, Log linear	Yes (Click GitHub link)	Python
Edit & Imputation	Imputation in the sample survey on participation of Polish residents in trips	Poland	Survey data	CART, Random forest, Optimal weighted nearest neighbor, Support vector machine		R
Edit & Imputation	Machine learning for imputation	Germany	Survey data	K-nearest neighbors, Bayesian network, Random forest, Support vector machine		R
Edit & Imputation	Early estimates of energy balance statistics using machine learning	Belgium VITO		Lasso regression, Linear regression, Neural network, Random forest, Ridge regression	Yes (Click GitHub link)	Python
Edit & Imputation	Editing of Living Cost and Food Survey Income data	UK	Survey data	Decision tree, Random forest, Neural network
Edit & Imputation	Editing in the Italian Register of the Public Administration	Italy	Administrative data	Decision tree, Random forest		R
Edit & Imputation	Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints	Italy
Imagery Analysis	Address Register Automated Image Recognition (AIR) model	Australia	Aerial imagery	Convolutional neural network		R
Imagery Analysis	Learning statistical information from images: a proof of concept	Netherlands	Aerial imagery, Satellite imagery	Convolutional neural network		Python
Imagery Analysis	Arealstatistik Deep Learning (ADELE)	Switzerland	Satellite imagery, Administrative data	Convolutional neural network, Random forest	To be made available	Python	Land cover statistics, Land use statistics
Imagery Analysis	Use of Landsat satellite data for the mapping of urban areas in non-census years	Mexico	Satellite imagery	Convolutional neural network, Extra tree		Python
Imagery Analysis	Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning	UNECE