PinnedPublished inTDS ArchiveUsing R’s simputation to handle missing valuesHoles in our dataset can be solved by making intelligent inferences from a linear modelJul 25, 2021A response icon1Jul 25, 2021A response icon1
Published incodinginrDetecting multicollinearity — it’s not that easy sometimesBy Huey Fern TayJan 12, 2022Jan 12, 2022
Published inCodeXHow to analyze the NAs in multiple response surveysBy Huey Fern Tay, With Greg PageJan 6, 2022Jan 6, 2022
Published inNerd For Tech5 things I learned by analysing Kaggle’s 2020 Machine Learning and Data Science SurveyBy Huey Fern TayNov 18, 2021Nov 18, 2021
Published inTDS ArchiveA Little-Known Trick in Hierarchical Clustering: WeightsRecalibrate your dataset to answer a business questionOct 4, 2021A response icon2Oct 4, 2021A response icon2
Published inTDS ArchiveWhen is it ok to impute missing values with a zero?Substituting missing data with a zero can sometimes be justifiedAug 11, 2021A response icon1Aug 11, 2021A response icon1
Published inTDS ArchiveSubstituting missing data with the group average — why it’s good to be cautiousBy Huey Fern TayJul 9, 2021Jul 9, 2021
Published inTDS ArchiveWhen a crazy wind wipes away our records — what do you do?By Huey Fern TayJul 3, 2021Jul 3, 2021
