Abstract
This paper uses Python and its external data processing package to conduct an in-depth analysis machine study of Airbnb review data. Increasingly, travelers are now using Airbnb instead of staying in traditional hotels. However, in such a growing and competitive Airbnb market, many hosts may find it difficult to make their listings attractive among the many. With the development of data science, the author can now analyse large amounts of data to obtain compelling evidence that helps Airbnb hosts find certain patterns in some popular properties. By learning and emulating these patterns, many hosts may be able to increase the popularity of their properties. By using Python to analyse all data from all aspects of Airbnb listings, the author proposes to test and find correlations between certain variables and popular listings. To ensure that the results are representative and general, the author used a database containing many multidimensional details and information about Airbnb listings to date. To obtain the desired results, the author uses the Pandas, NLTK, and matplotlib packages to better process and visualize the data. Finally, the author will make some recommendations to Airbnb hosts based on the evidence generated from the data in many ways. In the future, the author will build on this to further optimize the design.
Highlights
Even though Airbnb has gained a lot of popularity since its inception, there is increasingly emerging voice stating that they would rather choose hotels than Airbnb
Based on the evidence found through data analysis, the author can make some suggestions to Airbnb hosts
The first part is for all hosts
Summary
Even though Airbnb has gained a lot of popularity since its inception, there is increasingly emerging voice stating that they would rather choose hotels than Airbnb. According to a news report, some reasons include inaccurate descriptions of properties, prices being not cheaper than hotels, and long response time when communicating with hosts [1]. Listings include all the information that describes the properties like price, description, and location. The last file is called listings which has much more data than the previous two files It contains all the information of every Airbnb listing in Los Angeles. Some valuable information is room types, host response time, review scores of each section from guests, room descriptions, etc. These three files can be connected through unique IDs, and the author can combine all these factors to find all the relations that existed in these data
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