Data
churn

churn

active ARFF public Visibility: public Uploaded 06-04-2017 by Pieter Gijsbers
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  • OpenML-CC18 study_135 study_144 study_98 study_99
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Author: Unknown Source: [PMLB](https://github.com/EpistasisLab/penn-ml-benchmarks/tree/master/datasets/classification), [BigML](https://bigml.com/user/francisco/gallery/dataset/5163ad540c0b5e5b22000383), Supposedly from UCI but I can't find it there. Please cite: A dataset relating characteristics of telephony account features and usage and whether or not the customer churned. Originally used in [Discovering Knowledge in Data: An Introduction to Data Mining](http://secs.ac.in/wp-content/CSE_PORTAL/DataMining_Daniel.pdf).

21 features

class (target)nominal2 unique values
0 missing
statenumeric51 unique values
0 missing
account_lengthnumeric218 unique values
0 missing
area_codenominal3 unique values
0 missing
phone_numbernumeric5000 unique values
0 missing
international_plannominal2 unique values
0 missing
voice_mail_plannominal2 unique values
0 missing
number_vmail_messagesnumeric48 unique values
0 missing
total_day_minutesnumeric1961 unique values
0 missing
total_day_callsnumeric123 unique values
0 missing
total_day_chargenumeric1961 unique values
0 missing
total_eve_minutesnumeric1879 unique values
0 missing
total_eve_callsnumeric126 unique values
0 missing
total_eve_chargenumeric1659 unique values
0 missing
total_night_minutesnumeric1853 unique values
0 missing
total_night_callsnumeric131 unique values
0 missing
total_night_chargenumeric1028 unique values
0 missing
total_intl_minutesnumeric170 unique values
0 missing
total_intl_callsnumeric21 unique values
0 missing
total_intl_chargenumeric170 unique values
0 missing
number_customer_service_callsnominal10 unique values
0 missing

62 properties

5000
Number of instances (rows) of the dataset.
21
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.
16
Number of numeric attributes.
5
Number of nominal attributes.
224.32
Mean of means among attributes of the numeric type.
-0.05
First quartile of skewness among attributes of the numeric type.
0.02
Average mutual information between the nominal attributes and the target attribute.
3.15
First quartile of standard deviation of attributes of the numeric type.
0.76
Average class difference between consecutive instances.
51.46
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.17
Second quartile (Median) of entropy among attributes.
0.59
Entropy of the target attribute values.
3.8
Average number of distinct values among the attributes of the nominal type.
0.08
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
0.14
Mean skewness among attributes of the numeric type.
65.28
Second quartile (Median) of means among attributes of the numeric type.
24.36
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
109.24
Mean standard deviation of attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
85.86
Percentage of instances belonging to the most frequent class.
4293
Number of instances belonging to the most frequent class.
0.45
Minimal entropy among attributes.
-0.01
Second quartile (Median) of skewness among attributes of the numeric type.
2.28
Maximum entropy among attributes.
-1.2
Minimum kurtosis among attributes of the numeric type.
14.29
Percentage of binary attributes.
17.31
Second quartile (Median) of standard deviation of attributes of the numeric type.
3.27
Maximum kurtosis among attributes of the numeric type.
2.77
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
2.08
Third quartile of entropy among attributes.
2499.5
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0.19
Third quartile of kurtosis among attributes of the numeric type.
0.05
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
76.19
Percentage of numeric attributes.
160.28
Third quartile of means among attributes of the numeric type.
10
The maximum number of distinct values among attributes of the nominal type.
-0.21
Minimum skewness among attributes of the numeric type.
23.81
Percentage of nominal attributes.
0.05
Third quartile of mutual information between the nominal attributes and the target attribute.
1.36
Maximum skewness among attributes of the numeric type.
0.75
Minimum standard deviation of attributes of the numeric type.
0.55
First quartile of entropy among attributes.
0.02
Third quartile of skewness among attributes of the numeric type.
1443.52
Maximum standard deviation of attributes of the numeric type.
14.14
Percentage of instances belonging to the least frequent class.
-0.02
First quartile of kurtosis among attributes of the numeric type.
47.82
Third quartile of standard deviation of attributes of the numeric type.
1.27
Average entropy of the attributes.
707
Number of instances belonging to the least frequent class.
9.33
First quartile of means among attributes of the numeric type.
3.49
Standard deviation of the number of distinct values among attributes of the nominal type.
0.18
Mean kurtosis among attributes of the numeric type.
3
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.

10 tasks

2807 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
2047 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
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