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Scan through all values which have a Nan count greater than 60% of the total number of rows \nhttps://www.kaggle.com/code/vishalkasa/feature-engineering-k-means\n\n2.)Remove the users who did not booked the hotel\nhttps://www.kaggle.com/code/jiaofenx/expedia-hotel-recommendations\n\n3.) Look at when the booking were made i.e weekdays vs Saturday \n\n4.) Example using K means and various plots for data understanding \nhttps://www.kaggle.com/code/putdejudomthai/expedia-exploratory-data-destination-search\n\n5.) Using chi-squared feature analysis as well as PCA analysis \nhttps://medium.com/@zander.b.tedjo/expedia-hotel-recommendations-using-machine-learning-9a8eccd4ecba\n\n\n# questions 13-05\nhow to evaluate scores? \n- from sklearn.metrics import ndcg_score\n- booking ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmbbrainz%2Fdatamining_expedia-ranking","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmbbrainz%2Fdatamining_expedia-ranking","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmbbrainz%2Fdatamining_expedia-ranking/lists"}