ML-powered analytics and predictive modeling for optimizing leasing marketplace operations.
Live Demo: https://pazz-ml.vercel.app
This project explores machine learning applications in the leasing marketplace domain, focusing on:
- Demand forecasting for rental inventory
- Pricing optimization models
- Tenant-property matching predictions
- Market trend analysis and insights
- Backend: Python, scikit-learn, pandas
- Frontend: Vite, TypeScript (visualization dashboard)
- Deployment: Vercel
This project prioritizes building robust ML pipelines with proper validation practices. The focus is on creating production-ready models that can drive real business value in the leasing marketplace, while maintaining best practices: proper train/test splits, cross-validation, and avoiding data leakage.
Exploratory machine learning pipelines designed for iterative experimentation and learning, with an emphasis on correct modeling fundamentals over premature accuracy claims.