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

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

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

🏠 Built a House Price Predictor. 🔎 Preprocessed housing data (handled missing values, log-transformed, encoded ocean proximity). 📊 Used features like rooms, bedrooms, population, households, income & location. ✅ Trained Random Forest Regressor with optimized parameters. 🌐 Deployed a Streamlit web app for real-time house price prediction.

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

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.

Repository for Kaggle Competition : House Prices : Advanced Regression Techniques

Used to analysis house data of Nanjing city

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.

Create an excel report that contains all the meaningful information such as relevant charts, pivot tables, etc. Mention all the variable which are highly correlated. Used the linear regression model to train and forecast the houses sold in the year 2017 based on 2016 data. Interpret essential findings from the model.

Residential property prices across three decades in Brunei Darussalam

End-to-end House Price Prediction using Advanced Regression techniques and Deep Learning (ANN) with comprehensive EDA.

A model is trained through random forest. Afterwards a new data is been added to predict the house price. You can change the feature as well. but when u change the feature u should change the data accordingly.

🏡 Predict house prices using machine learning (Linear, Random Forest) with full EDA, preprocessing and model evaluation.

The goal of this project is to answer the following question: Where is a “good place” to buy a house in France and at what price? see readme file for info.

A preliminary data driven study on Toronto house prices

CRISP-DM house price prediction for Bolton residential area (R programming language): data prep, model comparison(MLR/SVR/Tree/RF), and Random Forest deployment.