Data
Tour-and-Travels-Customer-Churn-Prediction

Tour-and-Travels-Customer-Churn-Prediction

active ARFF CC0: Public Domain Visibility: public Uploaded 02-06-2023 by Matthias Feurer
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A Tour & Travels Company Wants To Predict Whether A Customer Will Churn Or Not Based On Indicators Given Below. Help Build Predictive Models And Save The Company's Money. Perform Fascinating EDAs. The Data Was Used For Practice Purposes And Also During A Mini Hackathon, Its Completely Free To Use

7 features

Target (target)nominal2 unique values
0 missing
Agenumeric11 unique values
0 missing
FrequentFlyernominal2 unique values
60 missing
AnnualIncomeClassnominal3 unique values
0 missing
ServicesOptednumeric6 unique values
0 missing
AccountSyncedToSocialMedianominal2 unique values
0 missing
BookedHotelOrNotnominal2 unique values
0 missing

19 properties

954
Number of instances (rows) of the dataset.
7
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
60
Number of missing values in the dataset.
60
Number of instances with at least one value missing.
2
Number of numeric attributes.
5
Number of nominal attributes.
57.14
Percentage of binary attributes.
6.29
Percentage of instances having missing values.
0.9
Percentage of missing values.
0.56
Average class difference between consecutive instances.
28.57
Percentage of numeric attributes.
0.01
Number of attributes divided by the number of instances.
71.43
Percentage of nominal attributes.
76.52
Percentage of instances belonging to the most frequent class.
730
Number of instances belonging to the most frequent class.
23.48
Percentage of instances belonging to the least frequent class.
224
Number of instances belonging to the least frequent class.
4
Number of binary attributes.

1 tasks

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