ANALYSIS OF AIRBNB DATA: THE INFLUENCE OF TIME, DAY OF WEEK, AND CLIENT DISTRIBUTION
In this story, we will investigate a dataset from Airbnb, which can be found in this link.
The goal of the present analysis is to clarify some issues concerning the prices of Airbnb.
Here, we'll make a simple, yet relevant, data analysis, clarifying three business questions, discussing them, followed by a brief conclusion.
Data Analysis
The whole Data analysis process was performed in Python 3.6 and can be found in the following repository hosted in Github, here.
Outline of the Data Analysis:
- How does the total price of Airbnb vary over the first and second semesters of 2016?
- How does the total price of Airbnb vary for each day of the week for the first and second semesters of 2016?
- How does the mean price by the client is distributed over the first and second semesters of 2016?
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Below, it will be shown the answers to these questions along with insighful graphs.
First Question
In the plot below it can be seen the total amount spent by all clients for the first (left) and second semesters (right).
In the first semester, it can be clearly be seen a trend of quadratic increase of the price with time, while for the second semester it is rather constant, in spite of a slight increase in December.
Second Question
Below is shown the plot of the total Price for each day of the week for the first and second semesters.
It can be clearly seen that in the first semester the prices are evenly distributed over the days of the week, whereas for the second semester there is a significant increase for Monday and Tuesday.
Third Question
In the plots below, we can see histograms of the mean amount spent by a client. The distributions are clearly right-skewed, being rather similar in their shapes.
The mean, minimum and maximum values, as well as the standard deviation are given for each semester shown in the notebook. Essentially, during the second semester, there are larger mean and maximum values, but also accompanied but also by a broader standard deviation.
Conclusion
For the first semester of 2016, the total sales of Airbnb was increasing fastly, but in the second semester, it reached a plateau. The analysis of the total price by day of the week showed that in the first semester the sales are evenly distributed, while in the second semester they are biased toward Monday and Tuesday. Analysis of the amount spent by a client showed that in the second amount the mean and maximum values were larger than in the first semester, however, also more spread (larger standard deviation).
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