Table of Contents

1. Introduction
2. Installations
3. Files
4. Links and Acknowledgements
5. Conclusion and Findings

1. Introduction

This repository contains an analysis done on the AirBnB listings in 2019 in New York City. Follwing the CRISP-DM approach, I have identified some potential business queries and come up with the corressponding answers. The project gives you an insight about the distribution of properties both in terms of price and in terms of the type of accomodations in different neighbourhood groups in New York City. In my project, I will be answering the following questions.

1. Identifying the hosts with the highest number of listings in the City.
2. What are the average listing prices and the distribution of these prices in different neighbourhoods.
3. Analysing variations or percentage differences in Room types in different neighborhoods.
4. What are the potential hotspots if you are looking to list an AirBnB.

2. Installations

The following modules have been used in the work done in this Project.

1. Pandas: The module is used for data manipulation.
2. Matplotlib and Seaborn: For data visualizations.
3. Geopandas and Shapely: For converting and plotting Geopandas Dataframes

3. Files

The repository consists of a total of 4 Files.

1. This Readme file.
2. The Ipython Notebook NYC_Airbnb_Udacity. The files contains all the analysis and answers the questions discussed above.
3. The dataset used in the analysis AB_NYC_2019.csv
4. A CSV nynta.csv file containing the geographical mappings of all the neighborhoods of New York City.

4. Links and Acknowledgements

1. Udacity: This project is part of the data science nano degree I am doing in collaboration with Udacity.
2. Kaggle Dataset: The link to the AirBnB dataset.
3. City of Newyork Neighbourhood Mappings: This is where the neighbourhood mappings used in the analysis are taken from.

5. Conclusion and Findings

The results of the anaylsis and conclusions can be found in the following article. Muneeb Aizaz AirBnB NYC report