OpenML
Shipping

Shipping

active ARFF Publicly available Visibility: public Uploaded 27-01-2023 by Young Lee
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An international e-commerce company based wants to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers. The company sells electronic products

10 features

class (target)numeric2 unique values
0 missing
Customer_care_callsnumeric6 unique values
0 missing
Customer_ratingnumeric5 unique values
0 missing
Prior_purchasesnumeric8 unique values
0 missing
Discount_offerednumeric65 unique values
0 missing
Weight_in_gmsnumeric4034 unique values
0 missing
Warehouse_blocknominal5 unique values
0 missing
Mode_of_Shipmentnominal3 unique values
0 missing
Product_importancenominal3 unique values
0 missing
Gendernominal2 unique values
0 missing

19 properties

10999
Number of instances (rows) of the dataset.
10
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.
6
Number of numeric attributes.
4
Number of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
10
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.52
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
60
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
40
Percentage of nominal attributes.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
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