A data-driven analysis using Austrian real estate online platform data
Summary
Table of Contents
Real estate information of a particular region or location is not easy to come by. In this notebook we investigate an Austrian real estate data set which has been mined extracted over the course of a week from a large online portal. The data has already been pre-processed and can be geospatial investigated.
This app can be viewed live on Heroku at https://austrian-housing.herokuapp.com/
app.py
main dash app
requirements.txt
python modules that will be installed for the web application at build.
/assets
this directory is to serve the CSS files and images for the app. charts.py
is used for generating the figures.
layout.py
defines the html web layout, callbacks.py
handles all the callbacks and data_wrangling.py
is used
for all the data queries and date manipulation.
/data
contains the raw data files.
/data/geojson/vienna.geojson
geojson files with the geospatial data of Austria.
/nb
notebook used for data exploration and analysis.
runtime.txt
tells (the Gunicorn HTTP server) which python version to use (only needed for Heroku deployment)
Procfile
defines what type of process is going to run (Gunicorn web process) and the Python app entrypoint
(only needed for a deployment on Heroku)
.gitignore
- Change the current directory to the location where you want to clone the repository and run:
$ git clone https://github.com/AReburg/Austrian-Real-Estate-Analysis.git
- Make sure that the app is running on the local webserver before deployment. Setup your virtualenv (or don't) and ensure you have all the modules installed before running the app.
Install the modules from the requirements.txt
with pip3 or conda from a terminal in the project root folder:
pip install -r requirements.txt
conda install --file requirements.txt
(Anaconda)
Executing the notebook is tested on anaconda distribution 6.4.12. with python 3.9.13. To view the rendered geospatial charts of the Jupyter notebook go to nbviewer and copy the link.
-
Run the app from your IDE direct, or from the terminal in the projects root directory:
python app.py
-
It should be accessible on the browser
http://127.0.0.1:8050/
The main findings are summarized in a post. Feel free to contact me if you have any questions or suggestions.