Midterm Report Review (lz469) #86
Description
The aim of the project is to predict the Airbnb price and occupancy rate. They are training the model with data on property location, characteristics, and reviews of 3818 Airbnb listings in Seattle. The prediction results will instruct new owners to set prices and optimize revenue for both the property owner and Airbnb.
Three good things:
- The report has a clear logic and good format which make the reader easy to understand.
- The bubble plot is a good choice to demonstrate the price and geography relationship.
- It makes sense to approximate the occupancy rate with the number of reviews per month.
Three things to improve:
- You mention that there are total 93 features included in the data set and list the name for some of them by four categories. It will be great if you could provide more data exploration with some interesting findings or visualization plots. You can also describe your data based on the type: discrete, continuous, and nominal.
- I think you can include more details on how the 7 features are selected among your 30 discrete and continuous features. The plots in the report give a good overview of the price distribution and some specific feature analysis, however, you haven't told how such information contributes to your decision making and model development.
- Instead of simply dropping the the missing values in one or more columns (since your data set is not big), you could try different methods to handle the missing value (eg. assign the average to NA, or define your own function...).
I am looking forward to seeing more on your future work!