https://github.com/halacoded/link-backend
https://github.com/halacoded/link-backend
Last synced: 9 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/halacoded/link-backend
- Owner: halacoded
- Created: 2025-07-03T07:17:07.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-07-11T20:06:07.000Z (11 months ago)
- Last Synced: 2025-07-11T22:08:56.180Z (11 months ago)
- Language: JavaScript
- Size: 4.05 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# LINK. – AI-Powered Customer Retention Platform for Telecoms (BACKEND)
**LINK.** is a full-stack customer intelligence web platform designed to help telecom operators take action on behavioral insights, predict customer dissatisfaction, and improve retention. This project was developed during **Huawei x Kuwait University Internship Program Summer 2025** and is inspired by Huawei's **SmartCare** solution.
## Supervisors
- **Supervisor Trainee:** Dr. Essam Alruqobah
- **Supervisor Engineer:** Eng. Ali Alsairafi
- **Supervisor Huawei Site:** Eng. Rahaf Alhasan
## Machine Learning Integration
LINK. is built to interface with a Python-based churn prediction model trained on IBM’s public Telco Customer Churn dataset. The model uses classification techniques (Logistic Regression, XGBoost) with telecom-inspired feature engineering:
- Custom Metrics:
- **KQI**: Key Quality Indicators
- **SQM**: Service Quality Metrics
- **NPM**: Network Performance Metrics
- ML Stack:
- scikit-learn, XGBoost, Optuna (hyperparameter tuning)
- Performance metrics: Accuracy, Recall, F1-score
ML Repo: [View Machine Learning Model on GitHub](https://github.com/halacoded/Churn-Prediction-Model-Based-on-Huawei-SmartCare)
## Flask Microservice
The LINK. platform integrates with a dedicated **Flask microservice** that delivers real-time churn prediction results. It acts as a bridge between the frontend and the trained machine learning model—analyzing customer feature data and returning churn probability scores for both single and batch requests.
- Built with **Flask**, **scikit-learn**, and **XGBoost**
- Receives API requests from the Node.js backend
- Supports batch prediction through `.csv` file uploads
- Powers the **Predictions Page** for interactive chart rendering
FLASK Repo: [View Microservice on GitHub](https://github.com/halacoded/LINK-FLASK-Microserver)
## Tech Stack
| Layer | Technology |
| ------------ | -------------------------------- |
| Frontend | React.js |
| Backend | Node.js + Express |
| Database | MongoDB |
| Design Tools | Figma for UI/UX |
| Styling | CSS |
| API Layer | REST endpoints for model results |
| Deployment | Netlify |