https://github.com/danieldacosta/airbnb-analysis

Data analysis of AirBnb website history in the city of Rio de Janeiro
https://github.com/danieldacosta/airbnb-analysis

airbnb-analysis airbnb-website-history data-analysis

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Data analysis of AirBnb website history in the city of Rio de Janeiro

• Host: GitHub
• URL: https://github.com/danieldacosta/airbnb-analysis
• Owner: DanielDaCosta
• License: mit
• Created: 2020-03-22T18:43:00.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-02-10T23:09:24.000Z (almost 2 years ago)
• Last Synced: 2023-03-05T14:54:46.715Z (over 1 year ago)
• Topics: airbnb-analysis, airbnb-website-history, data-analysis
• Language: Jupyter Notebook
• Homepage:
• Size: 68.4 MB
• Stars: 5
• Watchers: 0
• Forks: 1
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Airbnb-Data Analysis

Data analysis of Airbnb website history for the city of **Rio de Janeiro, Brazil**. The goal of this project is to explain and show step by step a data preprocessing procedure with a real world problem.

You can also check the repo [Medium Post](https://medium.com/@danieldacosta_75030/rio-de-janeiro-airbnb-data-analysis-b43241102455).

## Table of Contents

1. [Installation](#installation)

2. [Project Motivation](#motivation)

3. [File Descriptions](#descriptions)

4. [Acknowledgements](#acknowledgements)

## Installation 


All the required Python libraries to run the python code are in *requirements.txt* file. Run ``` pip install -r requiremts.txt``` to install all the dependencies.

The code should run with no issues using Python versions 3.*

## Project Motivation 


The goal of this project was to retrive the maximum amount of information from a real world dataset. The dataset contains information from January 2010 until January 2020.

The project was focused on answering quentions through data. Based on the information presented on each of the datasets, it was possible to better understand:

1. How do prices change based on location (geographical pricing) ?

2. Does the host response rate affect his scores ?

3. Airbnb growth through the years in Rio de Janeiro

4. How do prices change based on the number of accommodates ?

## File Descriptions 


There is one notebook, *Data_Analysis.ipynb*, to showcase work related to the above questions. The notebook explains step by step each procedures that were used answer each one of the questions above.

The *CSV folder* contains both of the dataset that were used:

  * Detailed Listings data for Rio de Janeiro

  * Detailed Review Data for listings in Rio de Janeiro

The Images folder contains an image of the price vs location study, this image is also shown inside the jupyter notebook.

## Acknowledgements 

The dataset was downloaded from [indesideairbnb](http://insideairbnb.com/get-the-data.html). The data behind the project is sourced from publicly avaiable information from the Airbnb site.