A comprehensive data science project that analyzes salary trends in Egypt's tech industry and provides predictive modeling capabilities through an interactive web application.
This project tackles the common dilemma faced by job seekers: "What should I ask as expected salary?" by providing data-driven insights and machine learning predictions based on real market data from Egypt's software industry.
- 📊 Salary Analytics: Comprehensive analysis of salary trends across different roles, experience levels, and work types
- 🤖 ML-Powered Predictions: Random Forest model with R² > 0.8 for accurate salary predictions
- 🎨 Interactive Dashboard: Beautiful visualizations and filters for exploring salary data
- 🌐 Web Application: Django-based deployment with modern UI/UX
- 📈 Market Insights: Actionable insights for job seekers, employers, and HR professionals
Egyptian-Salary-Analytics/
├── predictor/ # Django app
│ ├── ml_models/ # Trained models (RandomForest + Scaler)
│ ├── preprocessing.py # Data preprocessing utilities
│ ├── views.py # Django views
| ├── static/ # CSS, JS, and assets
│ └── templates/ # HTML templates
├── mywork/ # Project documentation & notebooks
│ ├── notebooks/ # Jupyter notebooks for EDA & modeling
│ └── documentation/ # Complete project documentation
│ └── datasets/ # uncleaned dataset extracted from google form, cleaned dataset I worked on
| └── dashboard/ # Interactive dashboard files
I followed a structured approach documented in my Notion workspace:
🔗 Project Planning & Tracking - Notion
The planning covered:
- Business Objectives: Clear problem definition and success metrics
- Stakeholder Analysis: Job seekers, employers, HR teams, and analysts
- Timeline Management: 5-day structured development cycle
- Resource Planning: Tools, technologies, and deliverables
- Source: Egypt Tech Salaries 2025 Survey Data
- Format: Google Forms responses (~1,000 records)
- Original Data: Google Drive Link
- Data Cataloging: Initial schema analysis and quality assessment
- Data Cleaning: Handled inconsistent formats, missing values, and Arabic text
- Feature Engineering: Job title categorization, currency normalization, date parsing
- Preprocessing: Encoding, scaling, and feature selection
Explore the data through our comprehensive dashboard:
- Salary Distribution: By experience, job title, and work type
- Geographic Analysis: City-wise salary comparisons
- Trend Analysis: Market trends over time
- Interactive Filters: Drill down by multiple dimensions
- Algorithm: Random Forest Regressor
- Performance: R² Score > 0.8
- Features: 10 engineered features including job category, experience, location, work type
- Deployment: Integrated into Django web application
Features Used:
├── Years of Experience
├── Job Category (7 categories)
├── Company Country Type
├── Work Type (Remote/Hybrid/On-site)
├── Work Hours (Full-time/Part-time)
├── City
├── Currency
└── Date Components (Day/Month/Year)Experience the salary predictor:
- Modern UI/UX: Glassmorphism design with smooth animations
- Real-time Predictions: Instant salary predictions based on user input
- Responsive Design: Works seamlessly across all devices
- Input Validation: Comprehensive error handling and user feedback
- Backend: Django 4.x
- Frontend: HTML5, CSS3, JavaScript (Vanilla)
- ML: scikit-learn, pandas, numpy
- Deployment: -
- Database: SQLite (development) / PostgreSQL (production)
Detailed project documentation including methodology, findings, and technical specifications:
📄 Complete Project Documentation
Explore the analysis and modeling process:
Python 3.8+
Django 4.x
pandas, numpy, scikit-learn# Clone the repository
git clone https://github.com/yourusername/egyptian-salary-analytics.git
cd egyptian-salary-analytics
# Install dependencies
pip install -r requirements.txt
# Run migrations
python manage.py migrate
# Start development server
python manage.py runserver# Create necessary directories
mkdir -p static/{css,js}
mkdir -p predictor/ml_models
mkdir -p mywork/{notebooks,documentation}
# Ensure models are in place
# Place your trained models in predictor/ml_models/- Experience Impact: Clear correlation between years of experience and salary
- Work Type Premium: Remote positions show 15-20% salary premium
- Geographic Variations: Significant salary differences across Egyptian cities
- Job Category Trends: Software development roles command highest salaries
- Currency Analysis: USD-paid positions significantly higher than EGP
- For Job Seekers: Data-driven salary expectations
- For Employers: Market-competitive compensation benchmarking
- For HR Teams: Industry-standard salary bands and trends
- Real-time Data Pipeline: Automated data updates from job portals
- Advanced ML Models: Deep learning approaches for improved accuracy
- Multi-country Support: Expand analysis to other MENA markets
- API Development: RESTful API for third-party integrations
- Mobile Application: Native iOS/Android apps
Contributions are welcome! Please feel free to submit pull requests or open issues for:
- Data quality improvements
- Model performance enhancements
- UI/UX improvements
- Documentation updates
Malak Ahmed Saber
- LinkedIn: [https://www.linkedin.com/in/malak-ahmed-saber-26a37b288/]
- Email: malak.a.saber88@gmail.com
This project is licensed under the MIT License - see the LICENSE file for details.
- Egypt's tech community for providing valuable salary data
- Open source contributors who made this analysis possible
- Survey participants who shared their compensation information
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Last updated: August 2025