Credit Risk Classification Dataset
Is Customer is Risky or Not Risky ?
@kaggle.praveengovi_credit_risk_classification_dataset
Is Customer is Risky or Not Risky ?
@kaggle.praveengovi_credit_risk_classification_dataset
This is Customer Transaction and Demographic related data , It holds Risky and Not Risky customer for specific banking products
Dataset is small in nature , It helps budding data scientist 👨‍🔬 👩‍🔬& Data Analyst to experiment Machine Learning and Statistical modelling concept
payment_data.csv: customer’s card payment history.
id: customer id
OVD_t1: number of times overdue type 1
OVD_t2: number of times overdue type 2
OVD_t3: number of times overdue type 3
OVD_sum: total overdue days
pay_normal: number of times normal payment
prod_code: credit product code
prod_limit: credit limit of product
update_date: account update date
new_balance: current balance of product
highest_balance: highest balance in history
report_date: date of recent payment
customer’s demographic data and category attributes which have been encoded.
Category features are fea_1, fea_3, fea_5, fea_6, fea_7, fea_9.
label is 1, the customer is in high credit risk
label is 0, the customer is in low credit risk
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This dataset help to find out weather customer is Credit Risky or Credit Worthy in Banking perspective
Q1 - What are the factors contributing to Credit Risky customer ?
Q2 - Behaviour of Credit Worthy Customer ?
CREATE TABLE customer_data (
"label" BIGINT,
"id" BIGINT,
"fea_1" BIGINT,
"fea_2" DOUBLE,
"fea_3" BIGINT,
"fea_4" DOUBLE,
"fea_5" BIGINT,
"fea_6" BIGINT,
"fea_7" BIGINT,
"fea_8" BIGINT,
"fea_9" BIGINT,
"fea_10" BIGINT,
"fea_11" DOUBLE
);CREATE TABLE payment_data (
"id" BIGINT,
"ovd_t1" BIGINT,
"ovd_t2" BIGINT,
"ovd_t3" BIGINT,
"ovd_sum" BIGINT,
"pay_normal" BIGINT,
"prod_code" BIGINT,
"prod_limit" DOUBLE,
"update_date" TIMESTAMP,
"new_balance" DOUBLE,
"highest_balance" DOUBLE,
"report_date" TIMESTAMP
);Anyone who has the link will be able to view this.