Contact person: Business Client

Phone:Show

Email:Show

Location: Pathankot, India

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I’m building EstateSmart, a web application that predicts fair property prices with an ML model that should reach roughly 85 % accuracy on a clean dataset of recent sales. The stack is React for the client side and Flask (Python 3.10) for the API layer, so everything must fit neatly into that architecture.

Core workflow
• Model: train, validate, and expose a price-prediction model via a REST endpoint.
• Explanations: every prediction returns both a concise text summary ​and​ a set of visual graphs (SHAP or LIME plots are ideal) so users immediately see “why” a price appears.
• UI: React pages for price search, locality trends, a three-property comparison panel, and an EMI calculator that uses interactive sliders for loan amount, tenure, and interest.
• Comparison: when users line up properties, they must always see size, number of rooms, and year built side-by-side; other fields can appear optionally.
• Deployment: containerise with Docker, stand up on AWS (Lightsail or EC2 is fine) behind HTTPS, and automate rebuilds through a simple GitHub Action.

I already have wireframes, a starter React theme, and a preliminary dataset; you’re free to refine them if it speeds modelling or UX. Success means:

1. End-to-end demo online (public URL)
2. Predictions within ±15 % of actual prices on a held-out test set
3. Valid, readable graphs and text explanations loading in under two seconds
4. No console or server errors in production mode

Once these are met I’ll close out the repo and mark the project complete. Let me know if you foresee any blockers around data quality or infrastructure; otherwise, push your proposed timeline and we can lock milestones." (client-provided description)