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 small approach to solving one of the many Kaggle problems

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

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

The missing guide to London properties

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

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

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

Project for UCL module CASA0006: Data Science for Spatial Systems. Exploring the Impact of Low Emission Zones on London House Prices

Used to analysis house data of Nanjing city

Repository for Kaggle Competition : House Prices : Advanced Regression Techniques

This repository includes my House Prices Multi-Variate Linear Regression-Flatiron School Module 2 Project. In this project I made use of the OSEMN methodology incorporating packages such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn.

Using Random Forest, XGBoost to precisely predict Housing Prices

A machine learning project focused on predicting house prices, featuring data preprocessing, model building, and deployment as a web application.