Seattle Airbnb Data Analysis

(Part of Udacity's Data Scientist Nanodegree using data from Airbnb) In this project, housing data from Seattly is analysed to provide meaningful answers to the following business questions following the CRISP-DM process:

• How does the time of the year influence the price of listings?
• How does the location influence the price of listings?
• How big is the discount for weekly and montly prices?

Those questions are answered in a blog post, which can be found here: https://medium.com/@mijka127/seattle-airbnb-data-analysis-f0c00c199129

Dependencies

The project is using Jupyther Notebook from the Anaconda distribution for Python3. Additionally, the following Python libraries will be used:

• Pandas
• Numpy
• Mathplotlib

Installing the project

1. Clone the project into your repository git clone https://github.com/michka127/udacity-data_scientist-Seattle_Airbnb_Data_Analysis.git
2. Unzip seattle_airbnb_data.zip in the project folder.

Running te project:

Open udacity_project_Seattle_Airbnb_Data_Analysis.ipynp using Jupyter Notebooks from the Anaconda distribution for Python3. Run the notebook.

Important folders and files

udacity_project_Seattle_Airbnb_Data_Analysis.ipynp: The Jupyther Notebook that does the analysis

plots: This folder contains all plots created during analysis

seattle_airbnb_data.zip: This archive contains the data provided from Airbnb:

• Listings.csv, including full descriptions and average review score
• Reviews.csv, including unique id for each reviewer and detailed comments
• Calendar.csv, including listing id and the price and availability for that day

Acknowledgements

The data used for the analysis was published by AirBnB dataset and is hosted by Kaggle. You can find it here: https://www.kaggle.com/airbnb/seattle/data

This project is part of the Udacity's Data Scientist Nanodegree programm: https://www.udacity.com/course/data-scientist-nanodegree--nd025