Midterm Report Review

[yz949]

The project intends to predict the next destinations of travelers by studying historical Airbnb data. The data has many important features, such as the traveler's previous destinations, their genders and so on. This is an interesting topic to discover as people are traveling more often than ever and travel agency is eager to have the most efficient models to predict their customers' behaviors.

After evaluating this report, I have summarized the following three things I like and three other things that could be improved.

Pros:

1. I like the way they cleaned their data. They first examined the overall data, dropping all empty or NA data. In addition, they also represent Gender feature as one-hot encoding.
2. I like their idea to investigate the different customer behaviors between OSX and Windows users. Their belief that Apple users tend to be willing to spend more on traveling expenses is very investing and worth investigating.
3. I like their "Moving Forward' section which shows clearly their next steps and also conclude a bit about their project process.

Cons:

1. The 3 histogram graphs shown in the 1st and 2nd page are lack of clear labels. It is hard to track the right information. It will be helpful if they can label some legends.
2. Regarding with the 'Preliminary Analysis" section, I feel more things should be discussed there. They talked about using 11 regularizations, however, I am not sure what these information tell you about their models.
3. This project is about a competition from Kaggle. While using some of the ideas from the available resources at Kaggle is helpful, I hope they can try to balance it so that to provide some different, insightful ideas and models.

Overall, the midterm report is pretty well written.