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The aim of this task is to leverage machine learning techniques to uncover developers' geographical location information. By analyzing developers' activity cycles and behavioral data within open-source communities, coupled with geographic data, you aim to establish an associative model between geographical locations and developer activities to identify developers' geographical locations. This identification method enables a better understanding of developers' geographical distribution, providing valuable information support for open-source community management, project collaboration, and more.
The relevant code and dataset for this task need to be provided in the repository.
The text was updated successfully, but these errors were encountered:
Description
The aim of this task is to leverage machine learning techniques to uncover developers' geographical location information. By analyzing developers' activity cycles and behavioral data within open-source communities, coupled with geographic data, you aim to establish an associative model between geographical locations and developer activities to identify developers' geographical locations. This identification method enables a better understanding of developers' geographical distribution, providing valuable information support for open-source community management, project collaboration, and more.
The relevant code and dataset for this task need to be provided in the repository.
The text was updated successfully, but these errors were encountered: