v0.2.0

Loan Score Prediction v0.2.0 Release Notes

Introduction

I'm excited to announce the latest update to Loan Score Prediction: version 0.2.0. This release introduces a significant architectural enhancement by splitting the backend and frontend, transforming the backend into an API. This major update increases flexibility and scalability, setting the stage for more dynamic features and integrations in the future.

What's New in v0.2.0

• Backend-Frontend Split: In a pivotal move, the project architecture has been restructured, separating the backend and frontend. The backend now operates as a standalone API, opening new avenues for dynamic interactions and integration capabilities.

• Model Insights: I have introduced a new method to extract and present the most important features of our predictive model. This addition offers deeper insights into the factors influencing loan repayment probabilities, enhancing the understanding of our model’s decision-making process.

Roadmap for Future Enhancements

• Advanced API Features: Future plans include introducing more functionalities to the API for richer data interactions and processing capabilities.
• Continuous Improvement in Data Processing: I aim to continuously refine our data processing techniques to optimize model performance.
• Complete Refonte of the UI: Plans are underway for a complete overhaul of the user interface, focusing on creating a more intuitive, engaging, and user-friendly experience.

Your Feedback Matters

As the sole developer behind Loan Score Prediction, your feedback is invaluable. I warmly welcome any suggestions, insights, or contributions from users and the developer community to further enrich this project.

v0.1.1

Loan Score Prediction v0.1.1 Release Notes

Introduction

I'm thrilled to introduce the second release of Loan Score Prediction version 0.1.1. This release aimed to increase the robustness, maintainability and automatization of the project.

Key Features

• Simplified User Interface: Developed with ease of use in mind, enabling straightforward interactions with the prediction model.
• Loan Repayment Predictions: Employs a sophisticated Random Forest algorithm to assess the probability of loan repayments by clients.

Recent Enhancements

• Extensive Documentation: Added detailed documentation to enhance clarity and usability for developers and users.
• Project Restructuration: Implemented a major restructure of the project to improve organization and maintainability.
• Unit Testing: Introduced unit tests, ensuring robustness and reliability of the codebase.
• GitHub Actions Integration: Integrated GitHub Actions for automated testing and continuous integration, enhancing code quality and development workflow.

Roadmap for Future Enhancements

• Rework of the UI: Plans to revamp the user interface for an even more intuitive and engaging user experience.
• Advanced Data Processing: Aiming to implement more sophisticated data processing techniques to further enhance the model's accuracy and efficiency.

Your Feedback Matters

As this is a solo project, I greatly value any feedback or suggestions from users and the developer community. Your insights can significantly contribute to the project's growth and improvement.

v0.1.0

I'm thrilled to introduce the first release of Loan Score Prediction version 0.1.0. This initial launch lays the groundwork for a tool designed to predict the likelihood of clients repaying loans. It features a simple yet functional user interface and is powered by a robust Random Forest model for prediction accuracy.

Key features

• Simplified User Interface: Developed with ease of use in mind, enabling straightforward interactions with the prediction model.
• Loan Repayment Predictions: Employs a sophisticated Random Forest algorithm to assess the probability of loan repayments by clients.

Roadmap for Future Enhancements

As the sole developer of "Loan Score Prediction," I'm actively considering several enhancements for future updates:

• Decoupling Front-End and Back-End: Plans to separate the user interface from the back-end processing, potentially employing Flask for the back-end to enhance the application's scalability and maintenance.
• Advanced Data Processing: Aiming to implement more refined data treatment techniques, including the removal of outliers and the use of statistical approaches (mode, median) for data normalization.
• Unit Testing: Introduction of a unit testing framework to ensure the reliability and robustness of the code, facilitating smoother future developments.
• CI/CD Pipeline: Exploring the integration of Continuous Integration and Continuous Deployment to streamline the development process, enabling faster and more reliable updates.

Your Feedback Matters

As this is a solo project, I greatly value any feedback or suggestions from users and the developer community. Your insights can significantly contribute to the project's growth and improvement.