Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

Β© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

dhahbimohamed/Predict_Tunisian_House_Rent

Domain:

socioeconomic
Creator:
dha
Host:
# πŸ‡ΉπŸ‡³ Tunisian House Rent Price Predictor 🏠 This is a Machine Learning-powered web app that predicts the **monthly rent price of houses in Tunisia**, based on surface area, number of rooms, number of bathrooms, and city. Built by a Tunisian CS student to explore real-world ML from end to end β€” from messy data cleaning to model training to web app deployment. --- ## πŸš€ Live App πŸ‘‰ Click here to open the live app --- ## πŸ“¦ Features - πŸ” Predicts rent price based on: - Surface (mΒ²) - Rooms - Bathrooms - City (one-hot encoded) - πŸ“Š Trained on real scraped data from Tayara.tn - 🎯 Supports Tunisian cities like Tunis, Sfax, Sousse, etc. - 🌐 Deployed with Streamlit Cloud for public use --- ## πŸ“ Dataset The dataset was scraped from tayara.tn and cleaned manually: - Removed entries with missing or inconsistent values - Filtered unreasonable outliers (e.g., 0 rooms with 1000 TND) - Encoded categorical data (cities) using One-Hot Encoding Final dataset: **~1900 rental houses** --- ## 🧠 Model We tested: - Linear Regression - Random Forest Regressor Final performance (after filtering outliers): - **Linear Regression** (best model βœ…) - **MAE**: ~244 TND - **RMSE**: ~302 TND Model trained on features: ```python ['surface', 'rooms', 'bathrooms', 'city_Tunis', 'city_Sfax', ...] ``` ## πŸ‘¨β€πŸ’» Author **Mohamed Dhahbi** β€” CS student & aspiring ML engineer πŸ“§ mohameddhahbi56@gmail.com

Visit

github.com

Languages

Arabic, Tunisian SpokenTachelhit