Peer Review by Tiffany Shih

[tiffanyshih]

Project Summary

This group's project sets out to answer two questions: Are there certain film features that determine how well it scores on IMDB? Can we predict box office success based on its background? The data being used is a kaggle data set of about 5000 movies from The Movie Database. The group's objective is to determine what might make or break a movie in the newly competitive film industry.

Three Things I Like About the Proposal

1. The group chose a really interesting topic to explore that could have a crucial impact on a huge part of the entertainment industry.
2. The group does a great job in defining their study. The proposal lists two very concrete goals that seem not only meaningful and practical but also achievable in the course of the semester.
3. In the last section of the proposal, it is evident that the group devoted significant time into analyzing the various features of this data set and how these features might impact their predictions.

Three Areas for Improvement

1. It would be helpful for the group to list some challenges they forsee in working with this dataset and how they plan to address them, in order to bolster their argument of why they believe they will be successful.
2. It seems like some features of the data set, such as directors and actors of each movie, will be categorical. It would be helpful to know how the group plans to address these features in their analysis. Will they try to transform the categorical data into something more concrete, such as quantifying the popularity of a certain actor or director?
3. The group may want to consider if the two questions they want to answer are mutually exclusive or have some correlation. Is box office success dependent on a high rating on IMDB?