Name: Finance Loan Approval Prediction Data
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Finance Loan Approval Prediction Data

Finance company deals in all loans. The customer first applies for a home loan after that company validates the customer's eligibility for a loan.

The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling out the online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem identifying the customer segments eligible for loan amounts to target these customers specifically. Here they have provided a partial data set.

Steps to Follow:

Problem Statement
Understanding the data
EDA
Perform Univariate & Bivariate Analysis
Missing value and outlier treatment
Evaluation Metrics for classification problem
Model building: Apply ML classification algorithms

Tables

Test

@kaggle.krishnaraj30_finance_loan_approval_prediction_data.test

15.57 kB
367 rows
12 columns

CREATE TABLE test (
  "loan_id" VARCHAR,
  "gender" VARCHAR,
  "married" VARCHAR,
  "dependents" VARCHAR,
  "education" VARCHAR,
  "self_employed" VARCHAR,
  "applicantincome" BIGINT,
  "coapplicantincome" BIGINT,
  "loanamount" DOUBLE,
  "loan_amount_term" DOUBLE,
  "credit_history" DOUBLE,
  "property_area" VARCHAR
);

Train

@kaggle.krishnaraj30_finance_loan_approval_prediction_data.train

20.65 kB
614 rows
13 columns

CREATE TABLE train (
  "loan_id" VARCHAR,
  "gender" VARCHAR,
  "married" VARCHAR,
  "dependents" VARCHAR,
  "education" VARCHAR,
  "self_employed" VARCHAR,
  "applicantincome" BIGINT,
  "coapplicantincome" DOUBLE,
  "loanamount" DOUBLE,
  "loan_amount_term" DOUBLE,
  "credit_history" DOUBLE,
  "property_area" VARCHAR,
  "loan_status" VARCHAR
);