Dataset: Equifax-pdl-scoring-US-2023.csv

About this Dataset

Equifax-pdl-scoring-US-2023.csv

This dataset is provided by the credit bureau for educational purposes and contains data from 2023. It includes information about 73,420 clients with various attributes to predict payday loan default. The dataset structure is as follows:

client_id: Unique identifier for each client
gender: Gender of the client (e.g., Male, Female)
age: Age of the client
education_level: Client's level of education (e.g., High School, Bachelor's)
owns_car: Indicates whether the client owns a car (1 for Yes, 0 for No)
car_category: Category of the car owned by the client (e.g., economy, luxury, etc.)
housing_status: Category of housing status (e.g., rent, own, etc.)
job_prestige: Prestige category of the client's job
stable_employment: Indicates whether the client has stable employment (1 for Yes, 0 for No)
annual_income: Client's annual income
travel_history: Indicates whether the client has a foreign passport and travel history
declined_applications: Number of previously declined loan applications
credit_score: Client's credit score based on bureau data
credit_inquiries: Number of credit inquiries made by credit bureaus
branch_category: Category of the branch based on channel and location
social_networking: Client's connections with other bank clients
info_update_recency: Recency of information update by the client
application_date: Date when the loan application was submitted (in datetime format with Eastern Time Zone)
default_status: Indicates whether the client has defaulted on a loan (1 for Default, 0 for No Default)

The dataset provides a comprehensive view of the client profiles, enabling detailed analysis and modeling for credit risk prediction.

EDA here

Tables

Equifax Pdl Us 2023

@kaggle.sokolovaleks_equifax_pdl_scoring_us_2023_csv.equifax_pdl_us_2023

1.72 MB
73420 rows
19 columns


CREATE TABLE equifax_pdl_us_2023 (
  "client_id" VARCHAR,
  "gender" VARCHAR,
  "age" BIGINT,
  "education_level" VARCHAR,
  "owns_car" BIGINT,
  "car_category" BIGINT,
  "housing_status" BIGINT,
  "job_prestige" BIGINT,
  "stable_employment" BIGINT,
  "annual_income" BIGINT,
  "travel_history" BIGINT,
  "declined_applications" BIGINT,
  "credit_score" BIGINT,
  "credit_inquiries" BIGINT,
  "branch_category" BIGINT,
  "social_networking" BIGINT,
  "info_update_recency" BIGINT,
  "application_date" VARCHAR,
  "default_status" BIGINT
);