datayoussef / jbi100_dashboard

NYC AirBnB Analysis

About this app

Welcome to our dashboard. The goal of this dashboard is to be able to analyze data from AirBnB accommodations. The intended end-user is an host that has accommodations listed on the AirBnB website.

Requirements

• Python 3 (add it to your path (system variables) to make sure you can access it from the command prompt)
• Git (https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)

How to run this app

We suggest you to create a virtual environment for running this app with Python 3. Clone this repository and open your terminal/command prompt in the root folder.

download a zip file of this folder, unzip it and copy it to a folder of choice on your computer

open a command prompt and run the following commands:

> cd <path to you folder of choice>\dashframework-main\dashframework-main 
> python -m venv venv

If python is not recognized use python3 instead

In Windows:

> venv\Scripts\activate

In Unix system:

> source venv/bin/activate

(Instead of a python virtual environment you can also use an anaconda virtual environment.

Requirements:

• Anaconda (https://www.anaconda.com/) or Miniconda (https://docs.conda.io/en/latest/miniconda.html)

• The difference is that Anaconda has a user-friendly UI but requires a lot of space, and Miniconda is Command Prompt based, no UI, but requires considerably less space.

Then you should replace the lines: python -m venv venv and venv\Scripts\activate or source venv/bin/activate with the following:

> conda create -n yourenvname
> conda activate yourenvname

)

Install all required packages by running:

> pip install -r requirements.txt

Run this app locally with:

> python app.py

You will get a http link, open this in your browser to see the results. You can edit the code in any editor (e.g. Visual Studio Code) and if you save it you will see the results in the browser.

Data preprocessing

Every action of cleaning is perfomed in the cleaning.py file. After cleaning the nan values and unnecessary attributes the dataset has been saved as a pickle file to avoid running the cleaning file every time. There's also some dataset exploration code (like descriptive statistics) at the end of the file. Codes in cleaning.py can be uncommented to see what changes have been made in the dataset. We also added a column with assigned neighbourhoods to make the accommodations compatible with the geojson geometry. (neighbourhood_fitter.py)

Code written by ourselves and "borrowed" code

app.py

We used the template code and made additions for our desired functionality and visualization. (Inherited initially from the framework)

pre_processing folder

Both files in this folder were written by us from scratch.

config.py

Written by ourselves. Holds out global configurations. (Inherited initially from the framework)

data.py

Written by ourselves. Holds the function that loads the data. (Inherited initially from the framework)

main.py

Title changed to our desired title. (Inherited initially from the framework)

menu.py

Changed according to our values and added two new dropdown menus. (Inherited initially from the framework)

scatterplot.py

Holds the definition of our plots (visualizations). Written by ourselves. (Inherited initially from the framework)

base.css

Not changed and originates from the framework as well.

style.css

Tweaked according to our preferred layout. (Inherited initially from the framework)

nyc-neighbourhoods.geo.json

Retreived from an external source as additional data (snd3 repo). See resources.

Resources

• Dash
• snd3 repository