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https://github.com/tushar2704/loan-limits-by-country
This project aims to leverage a diverse dataset encompassing economic indicators, demographic factors, and credit history to establish a predictive model. By establishing appropriate loan limits, financial institutions can enhance risk management, ensure responsible lending, and promote financial inclusivity.
https://github.com/tushar2704/loan-limits-by-country
artificial-intelligence data-analysis data-science loan project tushar2704
Last synced: about 2 months ago
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This project aims to leverage a diverse dataset encompassing economic indicators, demographic factors, and credit history to establish a predictive model. By establishing appropriate loan limits, financial institutions can enhance risk management, ensure responsible lending, and promote financial inclusivity.
- Host: GitHub
- URL: https://github.com/tushar2704/loan-limits-by-country
- Owner: tushar2704
- License: apache-2.0
- Created: 2023-08-15T08:26:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-08-15T08:33:02.000Z (over 1 year ago)
- Last Synced: 2024-05-11T05:53:46.655Z (9 months ago)
- Topics: artificial-intelligence, data-analysis, data-science, loan, project, tushar2704
- Language: Jupyter Notebook
- Homepage: https://tushar-aggarwal.com
- Size: 34.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Loan Limits by Country
**Problem Description:** The core objective of the "Loan Limits by Country" project is to create a model that can effectively determine suitable loan limits for individuals based on their respective countries. This project aims to leverage a diverse dataset encompassing economic indicators, demographic factors, and credit history to establish a predictive model. By establishing appropriate loan limits, financial institutions can enhance risk management, ensure responsible lending, and promote financial inclusivity.
## Project Structure
The project repository is organized as follows:
```
├── LICENSE
├── README.md <- README .
├── notebooks <- Folder containing the final reports/results of this project.
│ │
│ └── lona_limits.ipynb <- Final notebook for the project.
├── reports <- Folder containing the final reports/results of this project.
│ │
│ └── Report.pdf <- Final analysis report in PDF.
│
├── src <- Source for this project.
│ │
│ └── data <- Datasets used and collected for this project.
| └── model <- Model.
```
### Dataset Information
The project relies on the "LoanLimits_data" dataset, which incorporates a wide range of information relevant to lending practices in various countries. The dataset comprises the following key features:
- **Economic Indicators:** Variables such as Gross Domestic Product (GDP), inflation rate, unemployment rate, and income distribution within the country.
- **Demographic Factors:** Population density, average age, education levels, and employment sectors.
- **Credit History:** Historical credit scores, loan repayment behavior, and credit utilization patterns of individuals.
- **Country-Specific Variables:** Nation-specific attributes that could influence lending practices, including regulatory policies and cultural factors.
### Background Information
Determining appropriate loan limits is crucial for maintaining a balanced and sustainable lending environment. With the goal of promoting responsible lending and minimizing default risks, financial institutions need a data-driven approach to establish loan limits that are tailored to the economic and social landscape of each country. By analyzing the interplay between economic indicators, demographic trends, credit history, and country-specific factors, this project aims to contribute to the equitable distribution of credit and the fostering of financial stability.
### Project Workflow
1. **Data Exploration and Preprocessing:** The project begins with a comprehensive exploration of the dataset to identify data quality issues, outliers, and trends. Missing values are handled, and data normalization or scaling may be performed as necessary.
2. **Feature Engineering:** Relevant features are selected, and new features may be engineered based on domain knowledge and the relationships observed in the data.
3. **Model Development:** Various regression and classification algorithms are explored to create a model that accurately predicts appropriate loan limits. Algorithms such as Linear Regression, Decision Trees, Random Forests, and Neural Networks are evaluated.
4. **Model Evaluation:** The effectiveness of each model is assessed using appropriate evaluation metrics, such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), or precision-recall curves.
5. **Interpretability and Transparency:** Efforts are made to ensure that the model's decisions are explainable and transparent, particularly in the context of determining loan limits, which can have significant implications for individuals' financial well-being.
### Conclusion
The "Loan Limits by Country" project aspires to contribute to the responsible and inclusive lending practices of financial institutions. By employing a holistic approach that considers economic, demographic, and credit-related factors, the project seeks to generate insights that aid in establishing prudent loan limits for individuals in different countries. The ultimate goal is to strike a balance between providing access to credit and managing risks, thereby fostering financial stability and empowering individuals to achieve their financial goals responsibly.
## License
This project is licensed under the [MIT License](LICENSE).
## Author
- ©2023 Tushar Aggarwal. All rights reserved
- [LinkedIn](https://www.linkedin.com/in/tusharaggarwalinseec/)
- [Medium](https://medium.com/@tushar_aggarwal)
- [Tushar-Aggarwal.com](https://www.tushar-aggarwal.com/)
- [New Kaggle](https://www.kaggle.com/tagg27)
## Contact me!
If you have any questions, suggestions, or just want to say hello, you can reach out to us at [Tushar Aggarwal](mailto:[email protected]). We would love to hear from you!