Machine Learning of SECOM dataset (https://archive.ics.uci.edu/ml/datasets/SECOM) Following CRISP-DM Part 2: Getting and Cleaning Data

2. ●
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3. DataNames
# 590# 2
Class
Time- stamp
Unlabeled features
NaNs
FeaturesClass
# 590# 1
-1 or 1
19/07/2008
11:55:00
Year
Month
Day of Month
Day of Week
Hour
Minute
V1 to V590
Timestamp
# 6
NAs 4.54%
Today’s focus
Input Input Data frame

4. 116 28
Removed
Removed
#52
#28
CategoricalContinuous
#20 #116#454

5. Clean
#52
Upto 1% missing
#324
2 - 3%
#46
17%
#20
46%
#4
51-91%
#28
Removed

6. BACKUP

7. BACKUP

8. 9.5%
5.9%
4.1%
2.1%
% ErrorImputation Method
Mean
kNN (+39%)
Rpart (+30%)
PMM (+48%)
BACKUP

9. BACKUP

10. 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)

11. 46% NA
KNN impute

12. 10
1
1566
1545

15. BACKUP