Here is the Medium blog post I have written: https://medium.com/@emrebilgehangedik/seattle-airbnb-listings-analysis-d88c839596f8

This project (Write a Data Science Blog Post) is part of Udacity Data Scientists Nanodegree Program.

I used Seattle Airbnb Dataset for this project. As part of the Airbnb Inside initiative, the dataset describes the listing activities of homestays in Seattle, WA. The orijinal dataset can be found here: https://www.kaggle.com/airbnb/seattle

This project focuses on answering following questions: “What are the most common amenities in the listings?” "What attracts the guests most for renting a property?" “What are the most influencial features of the dataset to estimate the price of a listing?” “What is the impact of listings' proximity to Seattle Downtown in terms of price?

I use Python3. Here are the libraries I used in my Jupyter Notebook:

1. Numpy
2. Pandas
3. Sklearn
4. Seaborn
5. matplotlib.pyplot
6. Collections
7. Math

1. Seattle Airbnb Dataset.ipynb Jupyter notebook with complete analysis, answers to the questions, explanations and visualisations

2. listings.csv Original dataset in csv format