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

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.

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

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

The missing guide to London properties

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.

Used to analysis house data of Nanjing city

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

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.

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.

An analysis of house prices in Beijing

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 preliminary data driven study on Toronto house prices

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, the challenge is predict the final price of each home.

An example of selling houses