The Problem
Project management often suffers from unexpected delays, budget overruns, and lack of early risk visibility. Teams usually rely on manual estimation and experience, which leads to inaccurate planning and reactive decision-making instead of proactive risk handling.
Our Solution
We developed an AI-powered risk prediction system that analyzes project parameters and predicts potential delays and budget overruns. The system also generates intelligent risk alerts, enabling teams to take preventive actions early in the project lifecycle.
Solution Architecture
The solution follows a simple and scalable architecture:
User Input → Frontend (HTML/CSS/JS) → FastAPI Backend → ML Models → Predictions → Response Display
- Frontend collects project data
- Backend processes requests and calls ML models
- ML models generate predictions
- Results are returned and displayed dynamically
Deliverables
- Fully functional FastAPI backend
- Machine Learning models for:
- Delay prediction
- Budget estimation
- Rule-based risk alert system
- Interactive frontend (HTML, CSS, JavaScript)
- Swagger API documentation for testing
- End-to-end working POC
Tech Stack
Backend
- Python
- FastAPI
- Scikit-learn
- Pandas, NumPy
Frontend
- HTML
- CSS
- JavaScript (Vanilla)
Tools
- Swagger UI (API testing)
- Joblib (model saving)
Business Impact
This solution enables organizations to move from reactive to proactive project management. By predicting delays and budget risks early, teams can optimize resource allocation, reduce financial losses, and improve project success rates.
It is particularly valuable for industries like IT services, construction, consulting, and product development, where project timelines and budgets are critical. The system can help stakeholders make data-driven decisions, improve planning accuracy, and enhance overall operational efficiency.
