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DataNames

# 590# 2

Class

Time- stampUnlabeled features

NaNs

FeaturesClass

# 590# 1

-1 or 1

19/07/2008 11:55:00

YearMonth

Day of MonthDay of Week

Hour Minute

V1 to V590

Timestamp

# 6

NAs 4.54%

Today’s focus

Input Input Data frame

116 28

Removed

Removed

#52

#28Categorical Continuous

#20 #116#454

Clean#52

Upto 1% missing#324

2 - 3%#46

17%#20

46%#451-91%

#28

Removed

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9.5%

5.9%

4.1%

2.1%

% ErrorImputation Method

Mean

kNN (+39%)

Rpart (+30%)

PMM (+48%)

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Upto 1%#324

2 - 3%#46

17%#20

46%#4

Continuous Variables

Categorical Variables

5% trimmed mean#308

Mode #16

kNN imputation*

Clean #52

#48

#4

For each record, identify missing features. For each missing feature find the k nearest neighbors which have that feature. Impute the missing value using the imputation function on the k-length vector of values found from the neighbors. (Source: R package imputation v2.0.3 by Jeffrey Wong)

46% NA

KNN impute

10

11566

1545

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