Topic: housing-prices Goto Github
Some thing interesting about housing-prices
Some thing interesting about housing-prices
housing-prices,Implementation of 11 variants of Gradient Descent algorithm from scratch, applied to the Boston Housing Dataset.
User: aahouzi
housing-prices,Exploration and visualisation of the housing market in Denmark, using web scraping
User: adogb
housing-prices,ML model trained on data from Bayut.com to predict housing prices in Dubai
User: ahmed1996said
Home Page: https://dubai-housing.azurewebsites.net/
housing-prices,Data science project on Housing Prices Dataset regression analysis
User: ahmedshahriar
housing-prices,A repository of several Machine Learning Exercises that I completed of Andrew NG's course on Machine Learning
User: akashbanerjee
housing-prices,An attempt to classify properties in Edmonton based on their prices
User: ammarasmro
housing-prices,This model predicts housing price based on square feet, It has details comments which explain various terms
User: amogh-chavan
housing-prices,A library that enables programmatic interaction with daft.ie. Daft.ie has nationwide coverage and contains about 80% of the total available properties in Ireland.
User: anthonybloomer
housing-prices,An API to retrieve property price statistics in Ireland and the UK.
User: anthonybloomer
Home Page: https://anthonybloomer.github.io/smartmove-api
housing-prices,An API to allow developers to create applications using hyper local data on 27m homes, over 1m sale and rental listings, and 15 years of sold price data in the UK.
User: anthonybloomer
Home Page: https://anthonybloomer.github.io/zoopla/
housing-prices,Various topics with different sets of data.
User: atse0612
housing-prices,This repo uses Natural Langauage Processing, time series analysis, and ARIMA to explore predictive housing trend analysis.
User: benjaminweymouth
housing-prices,The repository contains tutorial code to predict Housing Price using Linear Regression in PyTorch
User: benuraj
housing-prices,Web crawler to collect housing information
User: chvieira2
Home Page: https://wgs-in-germany.streamlit.app/
housing-prices,Machine Learning Project based on a housing dataset from Ames, IA for a Kaggle.com competition
User: codesigma91
housing-prices,Using rayshader to visualise 3D data with Hong Kong map
User: cydalytics
Home Page: https://towardsdatascience.com/introducing-3d-ggplots-with-rayshader-r-c61e27c6f0e9
housing-prices,This one uses the NARX model to predict the forthcoming house price in months of 2017.
User: earthat
Home Page: https://www.free-thesis.com
housing-prices,R scripts for cleaning Immoscout24/RWI-GEO-RED data
User: eyayaw
housing-prices,Replication code for my paper `Geographic determinants and the price elasticity of housing supply in Germany`
User: eyayaw
housing-prices,Online calculator to predict the price of houses using the data collected from Ames, Iowa
User: gerardoj09
housing-prices,Python MLS and Real-Estate Data Scraper for the Realtor.ca Website
User: harry-s-grewal
housing-prices,An analysis of the housing and rent prices in the United States before and after the 2008 Housing Bubble.
User: harshbg
housing-prices,A machine learning project that explores and predicts the prices of houses in Washington, USA
User: hassan-ademola
housing-prices,Bayesian Market Segmentation Algorithm for Hedonic Analysis
User: hayato-n
housing-prices,Contains data visualization hands-on using Ames Housing Prices dataset from Kaggle. Done with tidyverse environment in R (Rstudio). Also was submitted as Homework Day 12 : Data Visualization in R .
User: indrayantom
housing-prices,Data scraped from various sites for housing data around the greater Toronto area (GTA). Scrapes happen daily and data is in both JSON and CSV formats. Free to use for analysis.
User: kennymkchan
housing-prices,A basic script in R for how to pull data from ImmobilienScout24's Rest Price History API
User: kylejott
housing-prices,Engineering Economy 课外拓展性研究小论文
User: liblaf
housing-prices,Heatmap visualization about prices of sold out houses in Andalucía
User: manuasir
housing-prices,Predict housing prices to help a startup complete a beta version of its product using Multivariate Linear Regression
User: merb92
housing-prices,Housing Prices Prediction in Colombia
User: miguelmque
housing-prices,Geospatial data analytics: Affects of community gardens on housing prices in New York City
User: msahamed
housing-prices,A tool for generating heatmaps from housing price data
User: olif
housing-prices,Predicting housing prices using various Machine Learning algorithms in Python.
User: rcjansonvtfl
housing-prices,A data analysis of the U.S. housing market using Zillow Research and Consumer Price Index (CPI) to compare housing costs and cost of living across multiple U.S. cities.
User: ricardo-hdz
housing-prices,Exploration of the impact of the peer-to-peer short-term rental industry to the housing market in San Francisco
User: rochiecuevas
housing-prices,An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.
Organization: rubixml
Home Page: https://rubixml.com
housing-prices,Housing price prediction with machine learning
User: samrat-halder
housing-prices,武汉东湖高新片区光谷&软件园二手房房价爬虫。data source: 房天下
User: shiqinhuo
housing-prices,House Price Prediction
User: shubh07k
housing-prices,A compilation of different models that predict a home's value (in Melbourne, Australia) and determine which model performs better and why.
User: stevenobadja
housing-prices,Machine Learning Nano-degree Project : To assist a real estate agent and his/her client with finding the best selling price for their home
User: sushantdhumak
housing-prices,R, Julia and Python implementation of the two submarket fully endogenized finite mixture model used in forthcoming articles by Fuad and Farmer (202-) and Fuad, Farmer, and Abidemi (202-).
User: syedmfuad
housing-prices,Kaggle Competition - House Prices: Advanced Regression Techniques
User: tiwari91
housing-prices,The app was designed around creating an AR iOS Application that implemented Zillow API, Google Cloud Platform, and also using Mongo DB for our database. This app allows users to receive basic housing information right at your fingertips.
User: trujamal
housing-prices,Data Visualisation application to show the distribution of property values across the City of Vancouver
Organization: ubc-mds
Home Page: https://ian-flores.shinyapps.io/vancouver_tax/
housing-prices,This repository consists of all different algorithms I applied on the various Datasets. This repository consists of simple python code for working on common datasets.
User: uragirii
housing-prices,Data: Boston Housing Dataset (HousingData.csv) Programming language(s): R Tool(s): RStudio Business problem: To understand the drivers behind the value of houses in Boston and provide data-driven recommendation to the client on how they can increase the value of housing.The Boston housing dataset consisted of 506 observations and 14 variables. Project challenge(s): MEDV (Median value of homes in Boston) was identified as the dependent variable. While the rest, were the independent variables. The goal was to find out which among the independent variables were statistically significant in driving the house prices (MEDV). The dataset consisted of missing values and outliers. Some of the variables had a skewed distribution. There was multicollinearity among few independent variables. Our Approach: Prior to model building, we tidied up our dataset by eliminating the rows that contained missing values. Replacing the missing values with median and mean of those variables were also done. Considering the three approaches, median imputation(replacing missing values with mean) was found to be the best approach. As the dependent variable "MEDV" (median value of houses) was continuous(numerical) in nature, we implemented the Multiple linear regression to build our model. Additional models were built from Decision trees and Random forest. On further investigation, we discovered that the dependent variable had a skewed distribution. By log transformation of this variable, we were able to get a normal distribution. Post transformation, we found out that the model built from Multiple linear regression with log transformed MEDV was the best in terms of MSE (Mean squared error) value and Adjusted R^2. All the assumptions of linear regression were met.
User: vishalv91
housing-prices,The study employs Moran’s I statistic and Local Indicators of Spatial Association (LISA) to analyze spatial patterns and dependencies in housing prices across U.S. counties.
User: yikuany
housing-prices,Analysis and prediction for the housing market prices using Cross Validation and Grid Search in several regression models
User: yjeong5126
Home Page: https://medium.com/@yjeong5126/predicting-housing-prices-in-melbourne-e3d5f49abf20
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.