This blog post is part of Udacity Data Scientists Nanodegree Program. Detailed analysis with all required code is posted in https://github.com/NTN-hacker/Write-a-Data-Science-Blog-Post
I will said that some information about my project: Link dataset: https://www.kaggle.com/datasets/airbnb/seattle/data
Introduction:
Since its inception in 2008, Airbnb has revolutionized the way we travel. No longer confined to hotels and traditional accommodations, travelers now have the option to experience cities in a unique and personalized manner, living like the locals. Through the Airbnb Inside initiative, we've been granted a peek into the listing activities in various cities. Today, let's explore the vibrant city of Seattle.
About the Dataset:
The dataset encapsulates the essence of Airbnb's presence in Seattle, Washington. Here's what it covers:
- Listings: Full descriptions of each listing. Average review scores, allowing potential guests to gauge the quality of a stay.
- Reviews: A unique ID assigned to each reviewer, ensuring data integrity and traceability. Detailed comments, offering a deep dive into guest experiences, both the good and the not-so-good.
- Calendar: A listing ID that correlates with the main listings dataset. Daily data points indicating the price and availability of each listing, helping to decipher patterns in bookings and pricing strategies.
Significance of the Data:
Such extensive data offers a plethora of opportunities for analysis:
- Market Analysis: Understand the ebb and flow of demand throughout the year.
- Pricing Strategies: Analyze how hosts price their properties and the factors influencing these decisions.
- Guest Feedback: Delve into reviews to gauge guest satisfaction and areas for improvement.
- Property Desirability: Find out which type of properties are most sought after, and which neighborhoods are the most popular.
Some analysis in my jupyter notebook:
Description some information about dataset Analysis to answer for 4 problems:
- Firstly, What are the most common amenities?
- Secondly, What is thing effect to the price? (Listing table)
- Thirstly, How to price their listings competitively based on their neighborhood, and guests can make informed decisions about where to stay based on their budget.
- Finally, Part 1: Identify Areas with the Highest Number of Positive Reviews and Part 2: Analyze Review Frequency Over Time
Link my jupyter notebook to see detail Notebook. If any problem is unclear, you could response for me via my email: nguyennhan8521@gmail.com Thank you.