Table of Contents

1. About The Project
2. Getting Started
3. Prerequisites
4. Python Libraries used
5. Installation
6. Project Motivation
7. Summary of Results
8. Blog
9. Contributing
10. Acknowledgments
11. File Description

Analyzing Seattle Airbnb Data

About The Project

The Goal of this project is to get insights from data about Airbnb business in Seattle and answer some questions.

Some of the questions we want to answer are:

1. When is the busiest time of the year and what is the average price at that time ?
2. Which neighborhoods are the most expensive ?
3. Which listing features influence price? (Build a predictive pricing model)
4. Does cancellatation policies have an effect on booking ?

Omoyeni Ogundipe, Analysing Seattle Airbnb Data, Udacity Data Science Project

Getting Started

The Data used can be gotten from kaggle It contains 3 csv files of CALENDAR, LISTINGS, REVIEWS

Prerequisites

Python3, Jupyter Notebook

Install and Import the necessary libraries

Python Libraries used :

• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn
• Folium
• SciPy
• lightgbm

Installation

• pandas

  pip install pandas

• Scikit-learn

  pip install -U scikit-learn

• Matplotlib

  pip install -U matplotlib

• Seaborn

  pip install seaborn

• Folium

  pip install folium

• Scipy

  pip install scipy

• lightgbm

  pip install lightgbm

  (back to top)

Project Motivation

This project was done as part of the Udacity Data Science Nanodegree program to challenge my data science skills by using data to gain insights, find trends and answer questions.

Summary of Results

Eventually, we were able to derive valuable insights and draw conclusive answers to some questions.

We were able to determine the busiest time of the year to be January, and we saw a trend of how bookings and prices decrease simultaneously during the year in Seattle. Also, we weren't able to establish a correlation between Price and cancelation policies.

(back to top)

Blog

READ MORE ON: WHAT DATA TELLS YOU ABOUT AIRBNB BUSINESS IN SEATTLE

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

(back to top)

Acknowledgments

• Airbnb image
• Udacity for the Data Science Program
• Airbnb and kaggle for the open data

(back to top)

File Description

• ReadMe
• Seattle : Contains the 3 csv files used for this project
• UdacityProj1.ipynb : Jupyter notebook file