The project analyzes the Seattle Airbnb dataset to provide insights into the city's neighborhoods, peak season for visiting, and trends in new listings and total visitors. The project aims to answer three questions:

1. Describe the vibe of each Seattle neighborhood using listing descriptions,
2. What are the busiest times of the year to visit Seattle? By how much do prices spike?
3. Is there a general upward trend to both new Airbnb listings and total Airbnb visitors to Seattle?

Natural language processing techniques, such as tokenization, countvectorizer, and removal of stopwords, are used to extract common themes of the neighborhoods from listing descriptions. Visualization and time series analysis are used to understand the busiest times, price hikes, and trends of new Airbnb listings and visitors to Seattle.

The project finds that the best time to visit Seattle on a budget is between February and April, and it identifies the neighborhoods' vibe through their listing descriptions. The project also shows an upward trend in new Airbnb listings and total visitors to Seattle.

The dataset can be found here: Kaggle

The following Airbnb activity is included in this Seattle dataset:

• Listings, including full descriptions and average review score
• Reviews, including unique id for each reviewer and detailed comments
• Calendar, including listing id and the price and availability for that day

1. Pandas
2. Matplotlib
3. Sklearn
4. Numpy

You will also need a software that can run a python notebook .ipynb

Check out my Medium post for results and analytical findings: https://medium.com/@adexseun13/analysis-of-seattle-airbnb-2016-data-fba7bd9b3bb1

This dataset was provided by Kaggle and can be downloaded here