Please go check the file ./Library/utils.R to see a set of utility functions that I (Chacoon3) wrote to speed up the machine learning workflow.
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This is the winner machine learning project of my master program's machine learning contest. It contains visualizations, datasets, documents, team-developed R libraries, trained models, as well as source codes.
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In this project, the repository owner along with other four teammates collaborated to train machine learning models that best ranks the popularity of Airbnb accomodations as were measured by a categorical target variable high_booking_rate.
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The most important codes of this project are in three files, namely main.r, utils.r, and data_cleaning.r. The latter two files are under the folder "Library".
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We finalized with a XGBoost model which had its AUC being 0.903, the highest AUC among all the teams that parcipated in this competition.