Name: Home Loan Approval
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Home Loan Approval

Problem Statement:

About Company
Dream Housing Finance company deals in all home loans. They have a presence across all urban, semi-urban and rural areas. The customer first applies for a home loan after that company validates the customer's eligibility for a loan.

Problem
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
Hypothesis Generation
Getting the system ready and loading the data
Understanding the data
EDA

Perform Univariate Analysis
Perform Bivariate Analysis

Missing value and outlier treatment
Evaluation Metrics for classification problem
Model building: part 1 (Apply ML classification algorithms)
Feature engineering
Model building: part 2 (Apply ML classification algorithms)

Tables

Loan Sanction Test

@kaggle.rishikeshkonapure_home_loan_approval.loan_sanction_test

15.57 kB
367 rows
12 columns

CREATE TABLE loan_sanction_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
);

Loan Sanction Train

@kaggle.rishikeshkonapure_home_loan_approval.loan_sanction_train

20.65 kB
614 rows
13 columns

CREATE TABLE loan_sanction_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
);