house-prices

Visualisation, annotation and powerful filtering tools for houses discovered on Hemnet.

Have you ever wanted to easily find the right house in the right place and that fits your budget? This real estate agency website is what you're looking for (if you live in Honduras); It was built in using JavaScript, Firebase, REST APIs, and other interesting technologies such as Cookies, Google Analytics and Intersection Observer

Interactive Map of Properties and Real Estate in Dhaka, Bangladesh, using data from BProperty.

A from-scratch Linear Regression model optimized via Gradient Descent for house price prediction.

A small approach to solving one of the many Kaggle problems

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

Ghana rental house price prediction using machine learning

An analysis of house prices in Beijing

• Created a House Price Prediction system using housing and location data with preprocessing, feature engineering, and Random Forest regression. • Deployed a Streamlit app for real-time price prediction.

Built a prediction model using both ridge and lasso advanced regression methods to predict house prices.

Scrape housing data from German housing portal Immowelt.de and retrieve as comma separted file.

This is an insight project to help in decision-making for buying and selling houses

This repository contains code for an end-to-end web application that predicts house prices. The app is built using Python and Flask, and includes a machine learning model that has been trained on a dataset of house prices.

Production-ready ML pipeline for regression tasks with modular architecture (0.94 R², Kaggle validated)

The missing guide to London properties

Decision-ready house price regression: leakage-safe CV, RMSE tracking, and reproducible pipeline in scikit-learn.

Detailed walkthrough of a data science project for the Kaggle House Prices challenge, covering data cleaning, EDA, feature engineering, and regression modeling.

Open-source MCP server for UK property market analysis. 12 tools: Rightmove listings and sold prices, daily market tracker (time-to-STC, fall-throughs, price cuts), Land Registry, House Price Index, flood risk, crime, listed buildings, air quality, schools and Ofsted ratings. All free public data.

Used to analysis house data of Nanjing city

A powerful mapping platform for visualizing data that shapes the UK.