This project is an academic data analysis and machine learning study that explores how Artificial Intelligence tools may influence software developers’ performance.
The objective is to analyze relationships between human factors (such as stress, sleep, and workload) and performance indicators, using Python-based data analysis techniques.
- Explore trends in developer performance data
- Analyze the impact of AI-assisted work
- Identify correlations between workload, stress, and success rate
- Apply basic machine learning models for performance prediction
- Python
- Pandas & NumPy for data analysis
- Matplotlib for visualization
- Scikit-learn for machine learning
- CSV datasets
The project follows a simple workflow:
- Data exploration and cleaning
- Basic statistical analysis
- Correlation analysis and visualization
- Machine learning experimentation
ProjectData/ │ ├── MachineLearning/ # Machine learning experiments ├── figures/ # Visualizations and plots ├── fichiers/ # Data processing files │ ├── Main.py # Main script ├── *.csv # Datasets and analysis outputs └── README.md # Project documentation
This project was developed collaboratively by:
- Fahd Amellal
- Rania Srir
- Aya Belghazi
- Wiam Chmicha
- Manar Messaoudi
- Chaimae Ghonim
This project is intended for educational purposes only and is based on a synthetic dataset.