Airbnb Seattle Dataset Analysis

Our work should present, how the marketing effectiveness of Airbnb can be enhanced by the analysis of a dataset of 2016. In order to improve the marketing, the four Ps of the marketing-mix should be addressed. The dataset contains listings of rented apartments and their attributes. The data for the first P, Product, is analyzed by using a WordCloud (obtained by using a Natural Language Processing) the 50 most mentioned words of all reviews on the apartments are displayed according to their neighborhood. Findings for the Price Policy are obtained by using Linear Regression a Neural Network and an Extra Tree classifier, which extracts the features that influence the price have been used. In order to find out when a marketing campaign should be started, the number of visitors over the year is analyzed and a prediction for 2017 is made by Linear Regression. Our findings show, that lessors should put words like “park”, “walk”, “lake” or “downtown” into their description to successfully address their target group. The price for an apartment is dependent of the attributes listed in the dataset, with number as review as attribute with the highest correlation. A good point in time to start a campaign would be February, as the number of visitors starts to decrease in March.

https://medium.com/@kingloehr2/airbnb-seattle-dataset-from-kaggle-400046a027df

• Python Notebook as iPython and HTML
• Report/Paper as PDF
• Presentation as PDF

Clone the repository, to get our Notebooks, Presentation and Project Report.

git clone https://github.com/Mavengence/Kaggle-Seattle-Airbnb-Analysis.git

• Of course you need git to get the source
• If you want to compile the report or the presentation by ur self u need a LaTex Compiler for your OS and maybe an IDE which makes things easier
• If you want to compile, train and play with our Code you need a python working environment. We used Jupyter Notebooks. The requiered packeges you can see in the Notebooks itself.
• Get the Dataset from Kaggle.com

cd/you_cloned_repo_location jupyter notebook

Just pull the repo, if you wanna change sth you can ask :)

• Tim Löhr - Coding, Report, Presentation - GitHub Mavengence
• Timo Bohnstedt - Coding, Report, Presentation - GitHub Bohniti
• List Nicole - Report, Presentation - GitHub kresnz
• Bal Kiran Jeniffer - Report - GitHub kirjen
• Pang Tsz Ching - Presentation -

See also the list of contributors who participated in this project.

Pretty much the BSD license, just don't repackage it and call it your own please! Also if you do make some changes, feel free to make a pull request and help make things more awesome!

The authors would like to thank Dr. Liu Junming from the City University of Hongkong for a really good supervising of our group. Dr. Liu helped us so much and we wouldn’t have reached this result without him.