Delinquency-Telecom-Dataset
OpenML dataset with id 43745
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22102570/Delinquency-Telecom-Dataset.arff
Upload date: 24 March 2022
Dataset Characteristics
Number of features: 35 (numeric: 33, symbolic: 0 and in total binary: 0 )
Number of instances: 209,593
Number of instances with missing values: 0
Number of missing values: 0
Context
Delinquency is a condition that arises when an activity or situation does not occur at its scheduled (or expected) date i.e., it occurs later than expected.
Content
Many donors, experts, and microfinance institutions (MFI) have become convinced that using mobile financial services (MFS) is more convenient and efficient, and less costly, than the traditional high-touch model for delivering microfinance services. MFS becomes especially useful when targeting the unbanked poor living in remote areas. The implementation of MFS, though, has been uneven with both significant challenges and successes.
Today, microfinance is widely accepted as a poverty-reduction tool, representing 70 billion in outstanding loans and a global outreach of 200 million clients.
Data Description
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox2F43788582F8d6c62159a033854dc4ca79d2cfbf0942FCapture.PNG?generation=1589482946434860alt=media
A Telecom collaborates with an MFI to provide micro-credit on mobile balances to be paid back in 5 days. The Consumer is believed to be delinquent if he deviates from the path of paying back the loaned amount within 5 days.
The sample data from our client database is hereby given to you for the exercise.
Exercise
Create a delinquency model which can predict in terms of a probability for each loan transaction, whether the customer will be paying back the loaned amount within 5 days of insurance of loan (Label 1 0)
This page was built for dataset: Delinquency-Telecom-Dataset
