5/30/2023 0 Comments Basic data science projectsbalance sheet) in an annual financial statement of a company. total assets) from the correct table (e.g. Statistics Canada's data scientists developed a state-of-the-art machine learning pipeline that correctly identifies and extracts key financial variables (e.g. For example, Statistics Canada has been experimenting with the historical dataset for SEDAR, a system used by publicly-traded Canadian companies, to file securities documents to various Canadian securities commissions. Statistics Canada has been applying data science solutions to extract information from PDFs and other documents in a timely and more efficient manner. Agriculture Greenhouse Detection Using Aerial Images The developed artificial intelligence algorithms might eventually lead to more accurate and timely data, while aiding in eliminating existing data gaps for the non-residential sector and small/remote communities excluded from the current survey. AI model building and evaluation required the processing of more than 1,400 km2 imagery of 50cm resolution over many months for which a highly scalable and efficient processing pipeline was created. The foundation is part of a structural system that supports and anchors the superstructure of a building. The process of pre-foundation consists of creating footings and concrete slabs to support the foundation walls, including excavation. These methods enabled data scientists to detect the area of the building in the pre-foundation and foundation phase. Statistics Canada has been employing various data science methods to detect construction starts from satellite images, such as using image augmentation to diversify and increase the data set. Image classification In-Season Crop ClassificationĬanadian Mortgage and Housing Corporation tracks the starts and completions of residential building construction projects across Canada, and results are used by Statistics Canada to calibrate estimates for its Investment in Building Construction program. With the move to CERS, electronic reporting is now mandatory and may result in an increase of cases with such inconsistencies, which is why an automated solution for review is being developed. The motivation for adding this validation is that analysis of the data from the previous systems revealed inconsistencies between the product description and the code chosen by the exporter. The Data Science Division, in partnership with the International Accounts and Trade Division (IATD), developed a FastText machine learning model to classify the additional text descriptions for the exported commodities to the HS codes so that IATD can use them to validate the self-coded HS codes provided by the exporters. CERS requires that an exporter self-code their goods' Harmonized System (HS) code plus an additional text description for more information for CBSA. The Canada Border Services Agency (CBSA) and Statistics Canada recently developed a new web-based reporting tool for Canadian exporters to non-US countries called the Canadian Export Reporting System (CERS).
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