Seattle_medium_post

medium post

This project was done as a requirement for the Udacity Nano degree program

to run the NB just fownlad it and use python 3 as the kernel

dataset : https://www.kaggle.com/airbnb/seattle/data

Libraries used

1. Pandas
2. Numpy
3. matplotlib
4. Seaborn
5. kepler-gl

Motivation for the project

My main motivations for analyzing the AirBnb dataset are

1. Which neighbourhoods have the highest number of listings?
2. Which neighbourhoods have the highest mean price per listing?
3. What makes superhosts different?
4. Why there was a fluctuation in the number of available listings over the year?

files in this repo

1. medium_post_seattle.ipynb : contains the code used to analyze and produce the results used in the Blogpost
2. Reamde: description of the repository
3. License file

Summary of the analysis

1. Most of the listings in Seattle are located in the Central region which happens to be the Downtown and Capital Hill region

2. Magnolia, Queen Anne, Downtown & West Seattle are most expensive neighborhoods.

3. Cheap neighborhoods like Delridge, Northgate, Rainer Valley have higher ratings

4. Number of Available listings dropped because more people were traveling Seattle in April through September

Acknowlegments

Kaggle for the dataset https://www.kaggle.com/airbnb/seattle/data

Python developers

Pandas developers

Seaborn developers

Matplotlib developers

Jupyter NB developers