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https://github.com/syedzaheerabbas/risk-analytics-with-python
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.
https://github.com/syedzaheerabbas/risk-analytics-with-python
eda hypothesis-testing numpy pandas python risk-analysis seaborn
Last synced: about 1 month ago
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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.
- Host: GitHub
- URL: https://github.com/syedzaheerabbas/risk-analytics-with-python
- Owner: Syedzaheerabbas
- Created: 2024-10-16T07:12:08.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-10-16T07:50:19.000Z (about 1 month ago)
- Last Synced: 2024-10-17T21:09:35.615Z (about 1 month ago)
- Topics: eda, hypothesis-testing, numpy, pandas, python, risk-analysis, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 13.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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](https://colab.research.google.com/drive/1li2QhpJ6fHJhvOSWXPBEfSAB8A7rD-S7#scrollTo=UUqOHrjdpfx0)
## PDF Report
A detailed analysis report is available in the following PDF file: [View Report](Risk_anlytics.pdf).