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This project focuses on developing a basic understanding of risk analytics in banking and financial services and understand how data is used to minimize the risk of losing money while lending to customers.

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Syedzaheerabbas/Risk-Analytics-with-Python

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Risk-Analytics-with-Python

Problem Statement:

  • Developing a basic understanding of risk analytics in banking and financial services and understand how data is used to minimize the risk of losing money while lending to customers.

Project Objectives:

  • Examining the impact of variables such as loan type, loan purpose, business or commercial nature, and credit score on loan defaults.
  • Investigating the correlation between upfront charges, loan amount, interest rates, and property values with the likelihood of default. -
  • Analyzing patterns and uncovering insights into default tendencies.

Data Description

Column Name Description
ID Unique identifier for each row
year Year when the loan was taken
loan_limit Indicates if the loan limit is fixed or variable: cf - confirm/fixed, ncf - not confirm/not fixed
Gender Gender of the applicant: male, female, not specified, joint (in case of applying as a couple)
loan_type Type of loan (masked data): type-1, type-2, type-3
loan_purpose Purpose of the loan (masked data): p1, p2, p3, p4
business_or_commercial Specifies if the loan is for a commercial establishment or personal establishment
loan_amount Amount of the loan
rate_of_interest Interest rate applied to the loan
Upfront_charges Down payment made by the applicant
property_value Value of the property for which the loan is taken
occupancy_type Occupancy type for the establishment
income Income of the applicant
credit_type Credit type of the applicant: EXP, EQUI, CRIF, CIB
Credit_Score Credit score of the applicant
co-applicant_credit_type Credit type of the co-applicant
age Age of the applicant
LTV Loan-to-value ratio of the applicant
Region Region of the applicant
Status Loan status: 1 - defaulter, 0 - normal

Methodology

  • Data loading and exploaration
  • Data cleaning
  • Feature Enginnering
  • Univariate Analysis
  • Bivariate Analysis
  • Multivariae Analysis
  • Impact of ddifferent variabes on defaulters
  • Insights
  • Key Findings
  • Recommendations

Colab Notebook

  • You can access the full Python analysis on Google Colab using the following link: View the notebook

PDF Report

A detailed analysis report is available in the following PDF file: View Report.

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This project focuses on developing a basic understanding of risk analytics in banking and financial services and understand how data is used to minimize the risk of losing money while lending to customers.

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