Data
blood-transfusion-service-center

blood-transfusion-service-center

active ARFF Publicly available Visibility: public Uploaded 21-05-2015 by Rafael G. Mantovani
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  • OpenML-CC18 OpenML100 study_123 study_135 study_14 study_34 study_50 study_52 study_7 study_98 study_99 uci
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Author: Prof. I-Cheng Yeh Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center) Please cite: Yeh, I-Cheng, Yang, King-Jang, and Ting, Tao-Ming, "Knowledge discovery on RFM model using Bernoulli sequence", Expert Systems with Applications, 2008. Blood Transfusion Service Center Data Set Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan -- this is a classification problem. To demonstrate the RFMTC marketing model (a modified version of RFM), this study adopted the donor database of Blood Transfusion Service Center in Hsin-Chu City in Taiwan. The center passes their blood transfusion service bus to one university in Hsin-Chu City to gather blood donated about every three months. To build an FRMTC model, we selected 748 donors at random from the donor database. ### Attribute Information * V1: Recency - months since last donation * V2: Frequency - total number of donation * V3: Monetary - total blood donated in c.c. * V4: Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood). The target attribute is a binary variable representing whether he/she donated blood in March 2007 (2 stands for donating blood; 1 stands for not donating blood).

5 features

Class (target)nominal2 unique values
0 missing
V1numeric31 unique values
0 missing
V2numeric33 unique values
0 missing
V3numeric33 unique values
0 missing
V4numeric78 unique values
0 missing

62 properties

748
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
4
Number of numeric attributes.
1
Number of nominal attributes.
Maximum entropy among attributes.
-0.25
Minimum kurtosis among attributes of the numeric type.
20
Percentage of binary attributes.
16.24
Second quartile (Median) of standard deviation of attributes of the numeric type.
15.88
Maximum kurtosis among attributes of the numeric type.
5.51
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1378.68
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
15.88
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
80
Percentage of numeric attributes.
1042.58
Third quartile of means among attributes of the numeric type.
2
The maximum number of distinct values among attributes of the nominal type.
0.75
Minimum skewness among attributes of the numeric type.
20
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
3.21
Maximum skewness among attributes of the numeric type.
5.84
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
3.21
Third quartile of skewness among attributes of the numeric type.
1459.83
Maximum standard deviation of attributes of the numeric type.
23.8
Percentage of instances belonging to the least frequent class.
2.16
First quartile of kurtosis among attributes of the numeric type.
1100.96
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
178
Number of instances belonging to the least frequent class.
6.51
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
10.22
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
356.99
Mean of means among attributes of the numeric type.
1.03
First quartile of skewness among attributes of the numeric type.
0.73
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
6.4
First quartile of standard deviation of attributes of the numeric type.
0.79
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0.01
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
12.63
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2.26
Mean skewness among attributes of the numeric type.
21.89
Second quartile (Median) of means among attributes of the numeric type.
76.2
Percentage of instances belonging to the most frequent class.
374.53
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
570
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
2.55
Second quartile (Median) of skewness among attributes of the numeric type.

18 tasks

376549 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
78885 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: chi-squared - target_feature: V2
45 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - target_feature: Class
1300 runs - target_feature: Class
1298 runs - target_feature: Class
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