Data
SantanderCustomerSatisfaction

SantanderCustomerSatisfaction

active ARFF Publicly available Visibility: public Uploaded 26-04-2020 by Arlind Kadra
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Author: Banco Santander Source: Unknown - 3-04-2019 Please cite: Unknown At Santander our mission is to help people and businesses prosper. We are always looking for ways to help our customers understand their financial health and identify which products and services might help them achieve their monetary goals. Our data science team is continually challenging our machine learning algorithms, working with the global data science community to make sure we can more accurately identify new ways to solve our most common challenge, binary classification problems such as: is a customer satisfied? Will a customer buy this product? Can a customer pay this loan? Dataset taken from Kaggle https://www.kaggle.com/c/santander-customer-transaction-prediction/data

202 features

target (target)nominal2 unique values
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ID_codestring200000 unique values
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var_0numeric94672 unique values
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19 properties

200000
Number of instances (rows) of the dataset.
202
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.
200
Number of numeric attributes.
1
Number of nominal attributes.
10.05
Percentage of instances belonging to the least frequent class.
20098
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
0.5
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0.82
Average class difference between consecutive instances.
99.01
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
0.5
Percentage of nominal attributes.
89.95
Percentage of instances belonging to the most frequent class.
179902
Number of instances belonging to the most frequent class.

8 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: target
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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