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Loan Defaulter Analysis- Exploratory Data Analysis and Feature Enginnering

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ExpLoan-Defaulter-Segmentation_EDA

DataSource- https://www.kaggle.com/datasets/gauravduttakiit/loan-defaulter

Problem Statement-

  1. Which type of customers should be targeted by the bamk who will be non-defaulters?
  2. What kind of interest rate bank can provide to these non-defaulter targeted customers?
  3. Make a thorough analysis and mention Recoomendations, Insights and Precautions

# All the analysis

most of the customers have taken cash loan

customers who have taken cash loans are less likely to default

CODE_GENDER -

 most of the loans have been taken by female

 default rate for females are just ~7% which is safer and lesser than male

NAME_TYPE_SUITE -

  unacompanied people had tanke most of the loans and the default rate is ~8.5% which is still okay

NAME_INCOME_TYPE -

   the safest segments are working, commercial associates and pensioners

NAME_EDUCATION_TYPE -

  Higher education is the safest segment to give the loan with a default rate of less than 5%

NAME_FAMILY_STATUS -

  Married people are safe to target, default rate is 8%

NAME_HOUSING_TYPE -

  People having house/appartment are safe to give the loan with default rate of ~8%

OCCUPATION_TYPE -

 Low-Skill Laboreres and drivers are highest defaulters

 Accountants are less defaulters

 Core staff, Managers and Laborers are safer to target with a default rate of <= 7.5 to 10%

ORGANIZATION_TYPE -

  Transport type 3 highest defaulter

  Others, Business Entity Type 3, Self Employed are good to go with default rate around 10 %

=======univariate numeric variables analysis========

 >> most of the loans were given for the goods price ranging between 0 to 1 ml
 
 >> most of the loans were given for the credit amount of 0 to 1 ml
 
 >> most of the customers are paying annuity of 0 to 50 K
 
 >> mostly the customers have income between 0 to 1 ml

=============bivariate analysis==================

 >> AMT_CREDIT and AMT_GOODS_PRICE are linearly corelated, if the AMT_CREDIT increases the defaulters are decreasing

 >> people having income less than or equals to 1 ml, are more like to take loans out of which who are taking loan of less than 1.5 million, coudl turn out to be defaulters. we can target income below 1 
    million and loan maount greater than 1.5 million
 
 >> people having children 1 to less than 5 are safer to give the loan
 
 >> People who can pay the annuity of 100K are more like to get the loan and that's upto less than 2ml (safer segment)

============analysis on merged data==============

 >> for the repairing purpose customers had applied mostly prev. and the same puspose has most number of cancelations
 
 >> most of the app. which were prev. either canceled or refused 80-90% of them are repayer in the current data

 >> offers which were unused prev. now have maximum number of defaulters despite of having high income band customers

# Final Conclusion/Insights

Bank should target the customers

 >> having low income i.e. below 1 ml
 
 >> working in Others, Business Entity Type 3, Self Employed  org. type
 
 >> working as Accountants, Core staff, Managers and Laborers 
 
 >> having house/appartment and are married and having children not more than 5
 
 >> Highly educated
 
 >> preferably female

 >> unacompanied people can be safer -  default rate is ~8.5%

Amount segment recommended -

 >> the credit amount should not be more than 1 ml

 >> annuity can be made of 50K (depending on the eligibility)

 >> income bracket could be below 1 ml

 >> 80-90% of the customer who were prev. canceled/refused, are repayers. Bank can do the analysis and can consider to give loan to these segments

====================precautions===============

 >> org. Transport type 3 should be avoided
 >> Low-Skill Laboreres and drivers  should be avoided
 >> offers prev. unused and high income customer should be avoided