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telco-customer-churn

telco-customer-churn

active ARFF Publicly available Visibility: public Uploaded 06-06-2023 by Matthias Feurer
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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)nominal2 unique values
0 missing
customerID (ignore)nominal7043 unique values
0 missing
gendernominal2 unique values
0 missing
SeniorCitizennominal2 unique values
0 missing
Partnernominal2 unique values
0 missing
Dependentsnominal2 unique values
0 missing
tenurenumeric73 unique values
0 missing
PhoneServicenominal2 unique values
0 missing
MultipleLinesnominal3 unique values
0 missing
InternetServicenominal3 unique values
0 missing
OnlineSecuritynominal3 unique values
0 missing
OnlineBackupnominal3 unique values
0 missing
DeviceProtectionnominal3 unique values
0 missing
TechSupportnominal3 unique values
0 missing
StreamingTVnominal3 unique values
0 missing
StreamingMoviesnominal3 unique values
0 missing
Contractnominal3 unique values
0 missing
PaperlessBillingnominal2 unique values
0 missing
PaymentMethodnominal4 unique values
0 missing
MonthlyChargesnumeric1585 unique values
0 missing
TotalChargesnumeric6530 unique values
11 missing

19 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).
11
Number of missing values in the dataset.
11
Number of instances with at least one value missing.
3
Number of numeric attributes.
17
Number of nominal attributes.
35
Percentage of binary attributes.
0.16
Percentage of instances having missing values.
0.61
Average class difference between consecutive instances.
0.01
Percentage of missing values.
0
Number of attributes divided by the number of instances.
15
Percentage of numeric attributes.
73.46
Percentage of instances belonging to the most frequent class.
85
Percentage of nominal attributes.
5174
Number of instances belonging to the most frequent class.
26.54
Percentage of instances belonging to the least frequent class.
1869
Number of instances belonging to the least frequent class.
7
Number of binary attributes.

1 tasks

0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: Churn
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