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shrutime

shrutime

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This data set contains details of a bank's customers and the target variable is a binary variable reflecting the fact whether the customer left the bank (closed his account) or he continues to be a customer.Source: https://www.kaggle.com/datasets/shrutimechlearn/churn-modelling

11 features

class (target)numeric2 unique values
0 missing
CreditScorenumeric460 unique values
0 missing
Agenumeric70 unique values
0 missing
Balancenumeric6382 unique values
0 missing
EstimatedSalarynumeric9999 unique values
0 missing
Geographynominal3 unique values
0 missing
IsActiveMembernominal2 unique values
0 missing
Tenurenominal11 unique values
0 missing
Gendernominal2 unique values
0 missing
HasCrCardnominal2 unique values
0 missing
NumOfProductsnominal4 unique values
0 missing

19 properties

10000
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
0
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.
5
Number of numeric attributes.
6
Number of nominal attributes.
54.55
Percentage of nominal attributes.
0.68
Average class difference between consecutive instances.
45.45
Percentage of numeric attributes.
0
Percentage of missing values.
0
Percentage of instances having missing values.
27.27
Percentage of binary attributes.
3
Number of binary attributes.
Number of instances belonging to the least frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the most frequent class.
0
Number of attributes divided by the number of instances.

2 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Define a new task