Data
Delinquency-Telecom-Dataset

Delinquency-Telecom-Dataset

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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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)

35 features

labelnumeric2 unique values
0 missing
msisdn (ignore)string186243 unique values
0 missing
aonnumeric4507 unique values
0 missing
daily_decr30numeric146328 unique values
0 missing
daily_decr90numeric155483 unique values
0 missing
rental30numeric131338 unique values
0 missing
rental90numeric139036 unique values
0 missing
last_rech_date_manumeric1186 unique values
0 missing
last_rech_date_danumeric1174 unique values
0 missing
last_rech_amt_manumeric70 unique values
0 missing
cnt_ma_rech30numeric71 unique values
0 missing
fr_ma_rech30numeric1083 unique values
0 missing
sumamnt_ma_rech30numeric15141 unique values
0 missing
medianamnt_ma_rech30numeric510 unique values
0 missing
medianmarechprebal30numeric23907 unique values
0 missing
cnt_ma_rech90numeric110 unique values
0 missing
fr_ma_rech90numeric89 unique values
0 missing
sumamnt_ma_rech90numeric31771 unique values
0 missing
medianamnt_ma_rech90numeric608 unique values
0 missing
medianmarechprebal90numeric22694 unique values
0 missing
cnt_da_rech30numeric1066 unique values
0 missing
fr_da_rech30numeric1072 unique values
0 missing
cnt_da_rech90numeric27 unique values
0 missing
fr_da_rech90numeric46 unique values
0 missing
cnt_loans30numeric40 unique values
0 missing
amnt_loans30numeric48 unique values
0 missing
maxamnt_loans30numeric1050 unique values
0 missing
medianamnt_loans30numeric6 unique values
0 missing
cnt_loans90numeric1110 unique values
0 missing
amnt_loans90numeric69 unique values
0 missing
maxamnt_loans90numeric3 unique values
0 missing
medianamnt_loans90numeric6 unique values
0 missing
payback30numeric1363 unique values
0 missing
payback90numeric2381 unique values
0 missing
pcirclestring1 unique values
0 missing
pdatestring82 unique values
0 missing

19 properties

209593
Number of instances (rows) of the dataset.
35
Number of attributes (columns) of the dataset.
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.
33
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
94.29
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage 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.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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