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.

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.

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

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

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