ayaelhassouni6-hash/Labor-Market-Dashboard

Full-stack dashboard analyzing Morocco's labor market — Python ML + Node.js/Express + MongoDB + Chart.js # 🇲🇦 Morocco Labor Market Analytics Dashboard Full-stack dashboard analyzing Morocco's unemployment trends (1991-2025) with a machine learning prediction model, built with Python, Node.js/Express, MongoDB, and Chart.js. ## Overview This project combines a data science pipeline with a full-stack web application: - **Python** cleans World Bank unemployment data and trains a prediction model - **MongoDB Atlas** stores the historical data and predictions - **Node.js/Express** exposes a REST API - **Vanilla JS + Chart.js** renders an interactive dashboard ## Live Demo - API: ` ` - Frontend: ` ` ## Key Insights - Morocco's unemployment rate dropped from ~13.5% (1991) to ~9% (2025) - The COVID-19 pandemic caused a sharp spike in 2020 (11.19%), later excluded from model training as it doesn't reflect the structural trend - A polynomial regression model (R² = 0.43) projects the rate could fall to ~6.8% by 2028 ## Tech Stack | Layer | Technology | |---|---| | Data cleaning / ML | Python, pandas, scikit-learn | | Database | MongoDB Atlas | | Backend / API | Node.js, Express | | Frontend | HTML, CSS, vanilla JavaScript, Chart.js | ## Project Structure labor-market-dashboard/ ├── data-pipeline/ # Python scripts: cleaning + ML model ├── server/ # Node.js/Express REST API ├── client/ # Frontend dashboard └── reports/ # Methodology and findings ## Installation ### Prerequisites - Python 3.10+ - Node.js 18+ - A MongoDB Atlas account (free tier) ### 1. Clone the repository ```bash git clone github.com cd Labor-Market-Dashboard ``` ### 2. Set up environment variables Create a `.env` file at the root: MONGODB_URI=your_mongodb_connection_string ### 3. Run the data pipeline (Python) ```bash cd data-pipeline python3 -m venv venv source venv/bin/activate pip install -r requirements.txt python clean_and_load.py python train_model.py ``` ### 4. Run the API (Node.js) ```bash cd ../serv …