Data
telco-customer-churn

telco-customer-churn

active ARFF Publicly available Visibility: public Uploaded 15-10-2019 by Andreas Mueller
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Context "Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets] Content Each row represents a customer, each column contains customer's attributes described on the column Metadata. The data set includes information about: Customers who left within the last month - the column is called Churn Services that each customer has signed up for - phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information - how long they've been a customer, contract, payment method, paperless billing, monthly charges, and total charges Demographic info about customers - gender, age range, and if they have partners and dependents Inspiration To explore this type of models and learn more about the subject. Taken from Kaggle: https://www.kaggle.com/blastchar/telco-customer-churn/download

20 features

Churn (target)string2 unique values
0 missing
customerID (ignore)string7043 unique values
0 missing
genderstring2 unique values
0 missing
SeniorCitizennumeric2 unique values
0 missing
Partnerstring2 unique values
0 missing
Dependentsstring2 unique values
0 missing
tenurenumeric73 unique values
0 missing
PhoneServicestring2 unique values
0 missing
MultipleLinesstring3 unique values
0 missing
InternetServicestring3 unique values
0 missing
OnlineSecuritystring3 unique values
0 missing
OnlineBackupstring3 unique values
0 missing
DeviceProtectionstring3 unique values
0 missing
TechSupportstring3 unique values
0 missing
StreamingTVstring3 unique values
0 missing
StreamingMoviesstring3 unique values
0 missing
Contractstring3 unique values
0 missing
PaperlessBillingstring2 unique values
0 missing
PaymentMethodstring4 unique values
0 missing
MonthlyChargesnumeric1585 unique values
0 missing
TotalChargesstring6531 unique values
0 missing

62 properties

7043
Number of instances (rows) of the dataset.
20
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.
3
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
24.56
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.39
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1.36
Maximum kurtosis among attributes of the numeric type.
0.16
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
1.36
Third quartile of kurtosis among attributes of the numeric type.
64.76
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
15
Percentage of numeric attributes.
64.76
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-0.22
Minimum skewness among attributes of the numeric type.
0.37
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.83
Third quartile of skewness among attributes of the numeric type.
1.83
Maximum skewness among attributes of the numeric type.
26.54
Percentage of instances belonging to the least frequent class.
-1.39
First quartile of kurtosis among attributes of the numeric type.
30.09
Third quartile of standard deviation of attributes of the numeric type.
30.09
Maximum standard deviation of attributes of the numeric type.
1869
Number of instances belonging to the least frequent class.
0.16
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.43
Mean kurtosis among attributes of the numeric type.
-0.22
First quartile of skewness among attributes of the numeric type.
32.43
Mean of means among attributes of the numeric type.
0.37
First quartile of standard deviation of attributes of the numeric type.
1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
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.
-1.26
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
32.37
Second quartile (Median) of means 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.
0.62
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
73.46
Percentage of instances belonging to the most frequent class.
18.34
Mean standard deviation of attributes of the numeric type.
0.24
Second quartile (Median) of skewness among attributes of the numeric type.
5174
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

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