VENT_AI is an exploratory machine learning project focused on analyzing ventilation-related data using AI techniques.
The project was developed as part of an academic and learning-oriented effort to understand how machine learning models can be applied in healthcare-related contexts, particularly ventilation systems.
The repository represents a prototype and experimentation phase, rather than a production-ready system.
The main objectives of this project were to:
- Explore ventilation-related data from a machine learning perspective
- Understand how healthcare or sensor data can be processed and modeled
- Experiment with different ML workflows for analysis and prediction
- Gain hands-on experience in applying AI techniques to real-world problem domains
This project focuses on:
- Data preprocessing and cleaning
- Feature exploration and basic analysis
- Initial model experimentation
- Observing model behavior and limitations
The emphasis was on learning and experimentation, not on building a finalized deployment solution.
The project follows a standard machine learning workflow:
- Data loading and inspection
- Preprocessing and feature preparation
- Initial model selection and training
- Basic evaluation and analysis
- Iterative experimentation
Exact model configurations may vary depending on experiments conducted during development.
- Python
- Machine learning libraries (e.g., scikit-learn / PyTorch)
- NumPy
- Pandas
- Matplotlib / visualization tools