rehan-ahmar/exploring-airbnb-data

Exploring AirBnB open data following CRISP-DM methodology

Exploring Airbnb open data following CRISP-DM methodology

Table of Contents

1. Project Motivation
2. Installation
3. File Descriptions
4. Results
5. Acknowledgements

Project Motivation

This project was created as part of Udacity's Data Scientist for Enterprise nanodegree. Here I have analyzed Seattle Airbnb Open Data following CRISP-DM methodology. Airbnb data for other cities have the same format. So the same understandings and code can be applied to Airbnb dataset of any other city.

The three business questions which I have tried to answer in this project are as follows:

• What is the seasonal price trend of Airbnb listings in Seattle? When are the most expensive and cheapest times to visit Seattle?
• How does price of Airbnb listings vary in different neigbourhoods?
• What are the most important factors influencing the price of Airbnb listings?

Installation

The code should run using any Python versions 3.*. I used python 3.6.

Libraries Used : numpy, pandas, matplotlib, seaborn, sklearn

If you don't have python already installed, I would suggest you to install the Anaconda distribution of Python, as you will get all the necessary libraries together.

File Descriptions

The analysis is divided into 4 files. The name of the files are self-explanatory. Each of the notebooks contains code and explains the detailed analysis performed to arrive at the below mentioned results for each of the questions showcased by the notebook titles.

• Business and Data Understanding.ipynb
• Question 1 - Seasonal price trend.ipynb
• Question 2 - Price trend by neighborhood.ipynb
• Question 3 - Factors influencing price.ipynb

Results

• Prices of Airbnb listings in Seattle are highest from July to September. The cheapest time is at the start of the year from January to March.
• The most expensive neighbourhoods in Seattle are Downtown, Magnolia, Queen Anne, Cascade and West Seattle. Capitol Hill and Downtown neighbourhoods have highest number of listings.
• The most important features which influence prices of Airbnb listings in Seattle are bedrooms, accommodates, bathrooms, beds - all indicating the size of the listing. Room type(Entire Apartment/House, Private Room or Shared Room) and reviews per month are also important features. Location also plays an important role. The most important amenities influencing price are Family/Kid Friendly, TV, Indoor Fireplace, Elevator in Building, Hot Tub, Gym and Kitchen.

For a more detailed non-technical discussion, check out my blog post.

Acknowledgements

Thanks to Airbnb and Kaggle for the data, and Udacity for course meterial.