{"id":17455505,"url":"https://github.com/syedzaheerabbas/risk-analytics-with-python","last_synced_at":"2026-04-17T01:31:52.475Z","repository":{"id":258150160,"uuid":"873441303","full_name":"Syedzaheerabbas/Risk-Analytics-with-Python","owner":"Syedzaheerabbas","description":"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.","archived":false,"fork":false,"pushed_at":"2024-10-16T07:50:19.000Z","size":13724,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-28T05:14:39.101Z","etag":null,"topics":["eda","hypothesis-testing","numpy","pandas","python","risk-analysis","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Syedzaheerabbas.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-10-16T07:12:08.000Z","updated_at":"2024-10-16T07:53:07.000Z","dependencies_parsed_at":"2024-10-17T21:09:41.164Z","dependency_job_id":"029c8749-828d-4700-aafc-634e7c90a63d","html_url":"https://github.com/Syedzaheerabbas/Risk-Analytics-with-Python","commit_stats":null,"previous_names":["syedzaheerabbas/risk-analytics-with-python"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Syedzaheerabbas%2FRisk-Analytics-with-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Syedzaheerabbas%2FRisk-Analytics-with-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Syedzaheerabbas%2FRisk-Analytics-with-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Syedzaheerabbas%2FRisk-Analytics-with-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Syedzaheerabbas","download_url":"https://codeload.github.com/Syedzaheerabbas/Risk-Analytics-with-Python/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245972748,"owners_count":20702723,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["eda","hypothesis-testing","numpy","pandas","python","risk-analysis","seaborn"],"created_at":"2024-10-18T02:04:24.741Z","updated_at":"2026-04-17T01:31:52.459Z","avatar_url":"https://github.com/Syedzaheerabbas.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Risk-Analytics-with-Python\n## Problem Statement:\n- 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.\n## Project Objectives: \n- Examining the impact of variables such as loan type, loan purpose, business or commercial nature, and credit score on loan defaults.\n- Investigating the correlation between upfront charges, loan amount, interest rates, and property values with the likelihood of default. -\n- Analyzing patterns and uncovering insights into default tendencies.\n\n## Data Description\n\n| Column Name               | Description                                                                                                  |\n|---------------------------|--------------------------------------------------------------------------------------------------------------|\n| **ID**                     | Unique identifier for each row                                                                               |\n| **year**                   | Year when the loan was taken                                                                                 |\n| **loan_limit**             | Indicates if the loan limit is fixed or variable: `cf` - confirm/fixed, `ncf` - not confirm/not fixed        |\n| **Gender**                 | Gender of the applicant: `male`, `female`, `not specified`, `joint` (in case of applying as a couple)        |\n| **loan_type**              | Type of loan (masked data): `type-1`, `type-2`, `type-3`                                                     |\n| **loan_purpose**           | Purpose of the loan (masked data): `p1`, `p2`, `p3`, `p4`                                                    |\n| **business_or_commercial** | Specifies if the loan is for a commercial establishment or personal establishment                            |\n| **loan_amount**            | Amount of the loan                                                                                           |\n| **rate_of_interest**       | Interest rate applied to the loan                                                                            |\n| **Upfront_charges**        | Down payment made by the applicant                                                                           |\n| **property_value**         | Value of the property for which the loan is taken                                                            |\n| **occupancy_type**         | Occupancy type for the establishment                                                                         |\n| **income**                 | Income of the applicant                                                                                      |\n| **credit_type**            | Credit type of the applicant: `EXP`, `EQUI`, `CRIF`, `CIB`                                                   |\n| **Credit_Score**           | Credit score of the applicant                                                                                |\n| **co-applicant_credit_type** | Credit type of the co-applicant                                                                             |\n| **age**                    | Age of the applicant                                                                                         |\n| **LTV**                    | Loan-to-value ratio of the applicant                                                                         |\n| **Region**                 | Region of the applicant                                                                                      |\n| **Status**                 | Loan status: `1` - defaulter, `0` - normal                                                                   |\n\n## Methodology\n- Data loading and exploaration\n- Data cleaning\n- Feature Enginnering\n- Univariate Analysis\n- Bivariate Analysis\n- Multivariae Analysis\n- Impact of ddifferent variabes on defaulters\n- Insights\n- Key Findings\n- Recommendations\n\n## Colab Notebook\n- 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)\n\n## PDF Report\n\nA detailed analysis report is available in the following PDF file: [View Report](Risk_anlytics.pdf).\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsyedzaheerabbas%2Frisk-analytics-with-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsyedzaheerabbas%2Frisk-analytics-with-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsyedzaheerabbas%2Frisk-analytics-with-python/lists"}