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https://github.com/stevehoober254/credit-recommendation-engine

The Credit Product Recommendation Engine
https://github.com/stevehoober254/credit-recommendation-engine

cicd-pipeline credit-analytics feature-engineering finetuning-llms flask-api machine-learning-algorithms mlflow mlops-project python-script react webscraping-data

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The Credit Product Recommendation Engine

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README 

          
# Credit Product Recommendation Engine



## Project Overview



The Credit Product Recommendation Engine is a sophisticated, machine learning-powered platform designed to deliver personalized credit product recommendations—such as credit cards, loans, mortgages, and insurance products—to both individual consumers and businesses. By leveraging comprehensive financial profiles, historical application data, and credit bureau integrations, the engine intelligently matches users with the best funding options tailored to their unique financial circumstances.



This project supports white labeling with flexible branding, offers interactive user features, and delivers actionable analytics to optimize recommendation success and platform performance. The architecture is built for scalability, security, and fast response times, making it suitable for modern financial technology ecosystems.



---



## Key Features



- **Personalized Recommendations:** Machine learning-driven credit product matching for optimal user fit based on credit score, income, location, loan history, and more.

- **Multi-Product Support:** Includes credit cards, personal and business loans, mortgages, and insurance products.

- **Data-Driven Insights:** Combines scraped credit product data, external credit reports (via SmartCredit API), and historical application approvals for enhanced accuracy.

- **Interactive User Experience:** Enables scenario analysis, feedback on recommendations, and rationale explanations to build trust and engagement.

- **White Label Ready:** Customizable branding to allow seamless integration into partner platforms with minimal overhead.

- **Robust Analytics Dashboard:** Tracks recommendation volume, conversion rates, geographic distribution, and most successful products to drive continual improvement.

- **Security & Compliance:** Adheres to strict data encryption, secure API access, and privacy regulation declarations to protect sensitive financial data.



---



## Technical Architecture



The solution consists of modular components designed for flexibility and maintainability:



- **Backend API Service:** Python-based RESTful API built with FastAPI, orchestrating data ingestion, model inference, and business logic.

- **Machine Learning Engine:** Hybrid recommendation model leveraging content-based and collaborative filtering, implemented with TensorFlow/PyTorch, retrained regularly to improve accuracy.

- **Data Ingestion Layer:** Scraping tools and connectors to external credit APIs ingest and normalize product and user data.

- **Frontend Interface:** React.js application presenting an intuitive web UI, supporting white-label theming and interactive features.

- **Analytics System:** Data pipelines and dashboard provide real-time monitoring and KPI reporting.

- **Cloud-Native Deployment:** Containerized microservices deployed on Kubernetes, ensuring high availability, scalability, and security.



![Architecture Diagram](credit_recommendation_architecture.png)



---



## Monorepo Structure



The project uses a modular monorepo layout to facilitate clean development and deployment pipelines:



```bash

credit-recommendation-engine/

│

├── backend/                        # Backend service code (API, business logic, ML model integration)

│   ├── app/

│   │   ├── api/                   # REST API endpoints and controllers

│   │   ├── core/                  # Core business logic, rules, and utilities

│   │   ├── models/                # Data models and schemas (user profiles, products, recommendations)

│   │   ├── ml/                    # ML model code, training scripts, inference logic

│   │   ├── services/              # Services for data ingestion, credit report integration, scraping

│   │   ├── config/                # Configuration files and environment variables

│   │   ├── db/                    # Database schema, migrations, ORM setup

│   │   ├── security/              # Authentication, authorization, encryption utilities

│   │   └── main.py                # API app entry point

│   ├── tests/                     # Backend unit and integration tests

│   ├── requirements.txt           # Python dependencies

│   └── Dockerfile                 # Container definition for backend service

│

├── frontend/                      # Web frontend code (React)

│   ├── public/                    # Public static assets

│   ├── src/

│   │   ├── components/            # Reusable UI components (input forms, results display, etc.)

│   │   ├── pages/                 # Top-level page components

│   │   ├── services/              # API client, utilities

│   │   ├── styles/                # CSS or styling files

│   │   ├── theme/                 # White label theming and branding

│   │   └── App.js                 # Root frontend component

│   ├── tests/                     # Frontend component tests

│   ├── package.json               # Node dependencies and scripts

│   └── Dockerfile                 # Container definition for frontend service

│

├── data-scraper/                  # Scraping scripts and data ingestion tools

│   ├── scrapers/                  # Individual scrapers for product sites

│   ├── parsers/                  # HTML parsing and data extraction logic

│   ├── utils/                    # Helper functions and utilities

│   ├── requirements.txt          # Dependencies for scraper tools

│   └── run_scraper.py            # Entry point to run data scraping

│

├── ml-models/                    # Machine learning model lifecycle

│   ├── training/                 # Training scripts, notebooks, datasets

│   ├── inference/                # Inference APIs or serving code

│   ├── experiments/              # Experiment tracking/logging scripts

│   ├── requirements.txt          # ML specific dependencies

│

├── analytics/                    # Analytics dashboard and reporting tools

│   ├── backend/                  # Analytics data processing backend

│   ├── frontend/                 # Dashboard frontend code

│

├── docs/                        # Documentation (technical, API specs, architectural diagrams)

│

├── deployment/                   # Deployment scripts, Helm charts, k8s manifests

│

├── scripts/                     # Utility scripts (db migrations, data backups, testing helpers)

│

├── .gitignore                   # Git ignore rules

├── docker-compose.yml           # Compose file to orchestrate multi-container apps locally

└── README.md                    # Project overview, setup instructions



---

```



## Technology Stack



- **Backend:** Python, FastAPI, PostgreSQL, Docker

- **Machine Learning:** TensorFlow/PyTorch, Scikit-learn, MLflow

- **Frontend:** React.js, Tailwind CSS / Material UI

- **Data Scraping:** Scrapy, Beautiful Soup

- **Infrastructure:** Kubernetes (EKS/GKE/AKS), AWS/GCP/Azure, API Gateway

- **Security:** OAuth2/JWT, TLS/SSL, AES-256

- **Analytics & Monitoring:** Prometheus, Grafana, ELK Stack



---



## Development & Deployment Workflow



- Code managed in a single monorepo with clear module boundaries.

- Containerized microservices support local development via Docker Compose.

- CI/CD pipelines handle testing, security scans, container builds, and deployment automation.

- Automated model retraining pipeline incorporated for continuous improvement.

- Infrastructure managed via Infrastructure as Code (IaC) using Helm and Kubernetes manifests.



---



## Performance & Scalability



- Designed to handle thousands of concurrent users with sub-second API response times.

- Supports scaling via Kubernetes Horizontal Pod Autoscaling and cloud-managed databases.

- Implements robust caching strategies for product data and model inferences.



---



## Security & Privacy



- End-to-end encryption of user data both in transit and at rest.

- Secure authentication and authorization mechanisms protecting API access.

- Compliance with relevant privacy laws and explicit user consent for data processing.

- Regular security audits and vulnerability assessments planned.



---



## What's Unique on this project



This engine combines advanced AI techniques with real-world financial data integrations, creating a cutting-edge tool for personalized funding solutions. Its modular and scalable architecture allows rapid customization and deployment, making it an ideal solution for fintech companies, credit bureaus, and banks aiming to enhance their digital offerings. The inclusion of interactive features and analytics ensures not only precision but continuous platform tuning based on actual user outcomes, demonstrating a commitment to both innovation and user-centric design.



---



## Getting Started



To start development or deployment:



1. Clone the repository.

2. Follow the scaffold setup script to initialize the project structure.

3. Install backend and frontend dependencies.

4. Configure environment variables for APIs, databases, and ML models.

5. Use Docker Compose for local service orchestration.

6. Run tests and validations.

7. Deploy to cloud platform using Helm charts.



---



## Contribution & Support



Developers and data scientists can contribute via clearly defined modules, with testing and CI workflows ensuring quality. Comprehensive documentation and API specs are maintained to guide both internal teams and external integrators.



---



## Contact



For inquiries, collaboration, or demonstrations, please contact [Steve](mailto:stephengachoka57@gmail.com)



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