Data
SantanderCustomerSatisfaction

SantanderCustomerSatisfaction

active ARFF Publicly available Visibility: public Uploaded 18-05-2020 by Arlind Kadra
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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
0 missing
ID_codenominal200000 unique values
0 missing
var_0numeric94672 unique values
0 missing
var_1numeric108932 unique values
0 missing
var_2numeric86555 unique values
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var_190numeric114959 unique values
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0 missing
var_192numeric59066 unique values
0 missing
var_193numeric110557 unique values
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var_194numeric97069 unique values
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var_195numeric57870 unique values
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var_196numeric125560 unique values
0 missing
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0 missing

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.
2
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
99.01
Percentage of numeric attributes.
89.95
Percentage of instances belonging to the most frequent class.
0.99
Percentage of nominal attributes.
179902
Number of instances belonging to the most frequent class.
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.
0.82
Average class difference between consecutive instances.
0
Percentage of missing values.

9 tasks

0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: target
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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